Decision support system using intelligent agents

ABSTRACT

A computing architecture, system and method are disclosed for use in a medical device for providing decision support to a caregiver. The computing architecture includes a memory, a processor in communication with the memory, and an instance of a primary rules-based service configured to provide instruction events, the instance providing a primary processing thread of instruction events for coaching treatment of a patient. A software manager module includes an artificial intelligence architecture. The artificial intelligence architecture is configured to provide an instance of a conditional rules-based service for providing instruction events. The instance provided by the artificial intelligence architecture provides a processing thread of instruction events for coaching treatment of a patient that is independent of the primary processing thread and is configured to trigger an action on the occurrence of a pre-defined set of input conditions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.14/513,961, filed Oct. 14, 2014, now U.S. Pat. No. 10,068,667, issued onSep. 4, 2018, which claims priority to U.S. provisional patentapplication No. 61/944,014, filed Feb. 24, 2014, the entire contents ofeach application incorporated herein by reference.

FIELD

This disclosure generally relates to decision support architectures formedical devices.

BACKGROUND

In humans, the heart beats to sustain life. In normal operation, itpumps blood through the various parts of the body. More particularly,the various chamber of the heart contract and expand in a periodic andcoordinated fashion, which causes the blood to be pumped regularly. Morespecifically, the right atrium sends deoxygenated blood into the rightventricle. The right ventricle pumps the blood to the lungs, where itbecomes oxygenated, and from where it returns to the left atrium. Theleft atrium pumps the oxygenated blood to the left ventricle. The leftventricle, then, expels the blood, forcing it to circulate to thevarious parts of the body and from where it returns to the right atriumto start the oxygenation-deoxygenation cycle of the blood all overagain.

The heart chambers pump because of the heart's electrical controlsystem. More particularly, the sinoatrial (SA) node generates anelectrical impulse, which generates further electrical signals. Thesefurther signals cause the above-described contractions of the variouschambers in the heart to occur in the correct sequence. The electricalpattern created by the sinoatrial (SA) node is called a sinus rhythm.

Sometimes, however, the electrical control system of the heartmalfunctions, which can cause the heart to beat irregularly, or not atall. The cardiac rhythm is then generally called an arrhythmia.Arrhythmias may be caused by electrical activity from locations in theheart other than the SA node. Some types of arrhythmia may result ininadequate blood flow, thus reducing the amount of blood pumped to thevarious parts of the body. Some arrhythmias may even result in a SuddenCardiac Arrest (SCA). In an SCA, the heart fails to pump bloodeffectively, and, if not corrected, can result in death. It is estimatedthat SCA results in more than 250,000 deaths per year in the UnitedStates alone. Further, an SCA may result from a condition other than anarrhythmia.

One type of arrhythmia associated with SCA is known as VentricularFibrillation (VF). VF is a type of malfunction where the ventricles makerapid, uncoordinated movements, instead of the normal contractions. Whenthat happens, the heart does not pump enough blood to deliver enoughoxygen to the vital organs. The person's condition will deterioraterapidly and, if not corrected in time, will result in death, e.g. withinten minutes.

Ventricular Fibrillation can often be reversed using a life-savingdevice called a defibrillator. A defibrillator, if applied properly, canadminister an electrical shock to the heart. The shock may terminate theVF, thus giving the heart the opportunity to resume normal contractionsin pumping blood. If VF is not terminated, the shock may be repeated,often at escalating energies.

A challenge with defibrillation is that the electrical shock must beadministered very soon after the onset of VF. There is not much time todo this since the survival rate of persons suffering from VF decreasesby about 10% for each minute the administration of a defibrillationshock is delayed. After about 10 minutes, the rate of survival for SCAvictims averages less than 2%.

The challenge of defibrillating early after the onset of VF is being metin a number of ways. First, for some people who are considered to be ata higher risk of VF or other heart arrhythmias, an ImplantableCardioverter Defibrillator (ICD) can be implanted surgically. An ICD canmonitor the person's heart, and administer an electrical shock asneeded. As such, an ICD reduces the need to have the higher-risk personbe monitored constantly by medical personnel.

Regardless, VF can occur unpredictably, even to a person who is notconsidered at risk. As such, VF can be experienced by many people wholack the benefit of ICD therapy. When VF occurs to a person who does nothave an ICD, they collapse, because the blood flow has stopped. Theyshould receive therapy quickly after the onset of VF or they will die.

For a VF victim without an ICD, a different type of defibrillator can beused, which is called an external defibrillator. External defibrillatorshave been made portable, so they can be brought to a potential VF victimquickly enough to revive them.

During VF, the person's condition deteriorates because the blood is notflowing to the brain, heart, lungs, and other organs. The blood flowmust be restored, if resuscitation attempts are to be successful.

Cardiopulmonary Resuscitation (CPR) is one method of forcing blood toagain flow in a person experiencing cardiac arrest. In addition, CPR isthe primary recommended treatment for some patients with some kinds ofnon-VF cardiac arrest, such as a systole and pulseless electricalactivity (PEA). CPR is a combination of techniques that include chestcompressions to force blood circulation, and rescue breathing to forcerespiration.

Properly administered CPR provides oxygenated blood to critical organsof a person in cardiac arrest, thereby minimizing the deterioration thatwould otherwise occur. As such, CPR can be beneficial for personsexperiencing VF, because it slows down the deterioration that wouldotherwise occur while a defibrillator is being retrieved. For patientswith an extended downtime, survival rates are higher if CPR isadministered prior to defibrillation.

A defibrillator is one medical device that is used to treat conditions.There are other medical devices including those recognized by the U.S.Food and Drug Administration for use in diagnosing, preventing, ortreating disease or other conditions.

Advances in medical devices have included provisioning the medicaldevices with hardware and software for generating alerts that providecoaching to a caregiver on a medical treatment. For example, a medicaldevice may issue instruction events, and even prompts, for the caregiverto perform CPR more effectively. Advanced medical devices may alsoprovide a caregiver with advanced coaching instructions on specificprocedures to follow for treating a patient for various conditions suchas sepsis, infection, bacteremia, SIRS, trauma, burns, pancreatitis,etc. Many advanced medical devices employ decision trees for navigatinga caregiver through the many and varied instructions that may make up aprotocol for the treatment of a particular condition of the patient.Caregivers may benefit from enhancements to these and other decisionsupport instructions and alerts that may make the coaching of acaregiver provided by the medical device more effective, efficient, andstrategic.

BRIEF SUMMARY

This disclosure is directed generally to providing intelligent agentsfor a decision support system thereby enhancing analysis through linkingand sharing information using knowledge and experience distributed amongintelligent agents and caregivers.

More specifically, a medical device includes a computing architectureincluding a memory, a processor, an instance of a primary rules-basedservice, and a software manager module including an artificialintelligence architecture. The processor is in communication with thememory. The instance of a primary rules-based service is configured toprovide instruction events, the instance providing a primary processingthread of instruction events for coaching treatment of a patient. Theartificial intelligence architecture of the software manager module isconfigured to provide an instance of a conditional rules-based servicefor providing instruction events. The instance of the artificialintelligence architecture provides a processing thread of instructionevents for coaching treatment of a patient that is independent of theprimary processing thread and is configured to trigger an action on theoccurrence of a pre-defined set of input conditions.

A medical system of this disclosure includes a medical device includinga computing architecture, an instance of a primary rules-based service,a software manager module including an artificial intelligencearchitecture, and an external utility. The medical device includes acomputing architecture that includes a memory, a processor incommunication with the memory; and a communication module. The instanceof a primary rules-based service is configured to provide instructionevents, the instance providing a primary processing thread ofinstruction events for coaching treatment of a patient. The artificialintelligence architecture of the software manager module is configuredto provide an instance of a conditional rules-based service forproviding instruction events. The instance of the artificialintelligence architecture provides a processing thread of instructionevents for coaching treatment of a patient that is independent of theprimary processing thread and is configured to trigger an action on theoccurrence of a pre-defined set of input conditions. The externalutility is configured for communication with the medical device forexchanging data between the medical device and the external utility.

A method of this disclosure provides decision support for a medicaltreatment. A primary processing thread of instruction events based on aprimary rules-based service is provided for coaching treatment of apatient. A processing thread of instruction events that is independentof the primary processing thread for coaching treatment of a patient andbased on a conditional rules-based service is provided by an artificialintelligence architecture for coaching treatment of a patient. Theprocessing thread of instruction events based on a conditionalrules-based service provided by an artificial intelligence architecturetriggers an action on the occurrence of a pre-defined set of inputconditions.

These and other features and advantages of this description will becomemore readily apparent from the following Detailed Description, whichproceeds with reference to the drawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustrative diagram of a scene showing the use of anexternal defibrillator to save the life of a person according to thisdisclosure.

FIG. 2 is a table listing two illustrative types of the externaldefibrillator shown in FIG. 1, and by whom they might be used.

FIG. 3 is a diagram showing components of an external defibrillator,such as the one shown in FIG. 1, configured in an illustrativeembodiment according to this disclosure.

FIG. 4 is an illustration of a prior art decision tree for the treatmentof a patient.

FIG. 5 illustrates a medical device including a computing architectureof this disclosure.

FIGS. 6A-6F show illustrative embodiments of a medical device includinga computing architecture of this disclosure.

FIG. 7 is an illustrative decision tree made possible by the decisionsupport system of this disclosure.

FIG. 8 is a prior art depiction showing some contributors of sepsis.

FIG. 9 shows a dynamically adaptive rule for determining whether apatient has sepsis using the artificial intelligence architecture ofthis disclosure.

FIG. 10 shows an illustrative logical expression of the rule detailed inFIG. 9.

FIG. 11 shows a prior art protocol for the detection and management ofsepsis.

FIG. 12 and FIG. 13 show an illustrative device alert from the decisionsupport system of this disclosure.

FIGS. 14A and 14B shows further illustrative embodiments of a medicalsystem including a computing architecture of this disclosure.

FIGS. 15 A-C show an illustrative embodiment of a process for theartificial intelligence architecture of this disclosure.

FIGS. 16 A-C is an illustrative embodiment of a process for the creationand distribution of artificial intelligence constructs, and/or updatesthereto, to medical devices according to this disclosure.

DETAILED DESCRIPTION

FIG. 1 is a diagram of a defibrillation scene showing the use of anexternal defibrillator to save the life of a person according to thisdisclosure. As shown, a person 82 is lying on his back. Person 82 couldbe a patient in a hospital, or someone found unconscious, and thenturned over onto his back. Person 82 is experiencing a condition intheir heart 85, which could be Ventricular Fibrillation (VF).

A portable external defibrillator 100 has been brought close to person82. At least two defibrillation electrodes 104, 108 are typicallyprovided with external defibrillator 100, and are sometimes calledelectrodes 104, 108. Electrodes 104, 108 are coupled together withexternal defibrillator 100 via respective electrode leads 105, 109. Arescuer (not shown) has attached electrodes 104, 108 to the skin ofperson 82. Defibrillator 100 is administering, via electrodes 104, 108,a brief, strong electric pulse 111 through the body of person 82. Pulse111, also known as a defibrillation shock, also goes through heart 85,in an attempt to restart it, for saving the life of person 82.

Defibrillator 100 can be one of different types, each with differentsets of features and capabilities. The set of capabilities ofdefibrillator 100 is determined based upon who would use it and whattraining they would be likely to have. Examples are now described.

FIG. 2 is a table listing two typical types of external defibrillators,and who they are primarily intended to be used by. A first type ofdefibrillator 100 is generally called a defibrillator-monitor, becausethe defibrillator part is typically formed as a single unit with apatient monitor part. A defibrillator-monitor is sometimes calledmonitor-defibrillator. A defibrillator-monitor is intended to be used bypersons in the medical profession, such as doctors, nurses, paramedics,emergency medical technicians, etc. who may be trained to providemedical treatment to the patient during a defibrillation process basedupon information provided by the monitor. Such a defibrillator-monitoris intended to be used in a pre-hospital or hospital scenario.

The defibrillator part may be dedicated to a particular mode ofoperation. Alternatively, the defibrillator part may be configured tooperate in more than one modes of operation. One mode of operation ofthe defibrillator part may be that of an automated defibrillator, whichcan determine whether a shock is needed and, if so, charge to apredetermined energy level and instruct the user to administer theshock. Another mode of operation may be that of a manual defibrillator,where the user determines the need and controls administering the shock.In this embodiment, one illustrative defibrillator is configured toenable both automated defibrillation and manual defibrillation modes ofoperation depending upon the selection of the user. As a patientmonitor, the device has features additional to what is minimally neededfor mere operation as a defibrillator.

These features can be for monitoring physiological indicators of aperson in an emergency scenario. These physiological indicators aretypically monitored as signals. For example, these signals can include aperson's full ECG (electrocardiogram) signals, or impedance between twoelectrodes. Additionally, these signals can be about the person'stemperature, non-invasive blood pressure (NIBP), arterial oxygensaturation/pulse oximetry (Sp02), the concentration or partial pressureof carbon dioxide in the respiratory gases, which is also known ascapnography, and so on. These signals can be further stored and/ortransmitted as patient data.

A second type of external defibrillator 100 is generally called an AED,which stands for “Automated External Defibrillator”. An AED typicallymakes the shock/no shock determination by itself, automatically. Indeed,it can sense enough physiological conditions of the person 82 via onlythe shown defibrillation electrodes 104, 108 of FIG. 1. In its presentembodiments, an AED can either administer the shock automatically, orinstruct the user to do so, e.g. by pushing a button. Being of a muchsimpler construction, an AED typically costs much less than adefibrillator-monitor. As such, it makes sense for a hospital, forexample, to deploy AEDs at its various floors, in case the moreexpensive defibrillator-monitor is more critically being deployed at anIntensive Care Unit, and so on.

AEDs, however, can also be used by people who are not trained in themedical profession. More particularly, an AED can be used by manyprofessional first responders, such as policemen, firemen, etc. Even aperson with only first-aid training can use one. And AEDs increasinglycan supply instruction events to whoever is using them. AEDs are thusparticularly useful, because it is so critical to respond quickly, whena person suffers from VF. Often, the people who will first reach the VFsufferer may not be in the medical profession.

Increasing awareness of the short survival time of a patientexperiencing a VF, has resulted in AEDs being deployed more pervasivelyin public or semi-public spaces, enabling members of the public to useone provided they have obtained first aid and CPR/AED training. In thisway, defibrillation can be administered sooner after the onset of VF, tohopefully be effective in rescuing the person.

There are additional types of external defibrillators, which are notlisted in FIG. 2. For example, a hybrid defibrillator can have aspectsof an AED, and also of a defibrillator-monitor. An illustrative examplemay be an AED provided with an ECG monitoring capability.

FIG. 3 is a diagram showing components of an external defibrillator 300configured in an illustrative embodiment according to this disclosure.These components can be configured, for example, in externaldefibrillator 100 of FIG. 1. Plus, these components of FIG. 3 can beprovided in a housing 301, which is also known as casing 301.

External defibrillator 300 is intended for use by a user 380, who wouldbe the rescuer. Defibrillator 300 typically includes a defibrillationport 310, which may be configured as a socket (not shown) in housing301. Defibrillation port 310 includes nodes 314, 318. Defibrillationelectrodes 304, 308, which can be similar to electrodes 104, 108 in FIG.1, can be plugged into defibrillation port 310, so as to make electricalcontact with nodes 314, 318, respectively. It is also possible thatelectrodes can be hard-wired to defibrillation port 310, etc. Eitherway, defibrillation port 310 can be used for guiding to person 82 viaelectrodes an electrical charge that has been stored in defibrillator300, as discussed below.

If defibrillator 300 is actually a defibrillator-monitor, as wasdescribed with reference to FIG. 2, then it will typically also have anECG port 319 in housing 301, for plugging in ECG leads 309. ECG leads309 can help sense an ECG signal, e.g. a 12-lead signal, or a signaltaken from a different number of leads. Moreover, adefibrillator-monitor could have additional ports (not shown), andanother component 325 for the above described additional features, suchas for receipt of patient signals.

Defibrillator 300 also includes a measurement circuit 320. Measurementcircuit 320 receives physiological signals from ECG port 319, and alsofrom other ports, if provided. These physiological signals are sensed,and information about them is rendered by circuit 320 as data, or othersignals, etc.

If defibrillator 300 is actually an AED, it may lack ECG port 319.Measurement circuit 320 can obtain physiological signals in this casethrough nodes 314, 318 instead, when defibrillation electrodes 304, 308are attached to person 82. In these cases, a person's ECG signal can besensed as a voltage difference between electrodes 304, 308. Plus,impedance between electrodes 304, 308 can be sensed for detecting, amongother things, whether these electrodes 304, 308 have been inadvertentlydisconnected from the person.

Defibrillator 300 also includes a processor 330. Processor 330 may beimplemented in any number of ways. Such ways include, by way of exampleand not of limitation, digital and/or analog processors such asmicroprocessors and digital-signal processors (DSPs); controllers suchas microcontrollers; software running in a machine; programmablecircuits such as Field Programmable Gate Arrays (FPGAs),Field-Programmable Analog Arrays (FPAAs), Programmable Logic Devices(PLDs), Application Specific Integrated Circuits (ASICs), anycombination of one or more of these, and so on.

Processor 330 may include a number of modules. One such module can be adetection module 332, which senses outputs of measurement circuit 320.Detection module 332 can include a VF detector. Thus, the person'ssensed ECG can be used to determine whether the person is experiencingVF.

Another such module in processor 330 can be an advice module 334, whicharrives at a piece of instructional advice based on outputs of detectionmodule 332. Advice module 334 can include a Shock Advisory Algorithmresiding in a memory unit (not shown) in the advice module forinstructing the processor to implement decision rules, etc.Alternatively, the Shock Advisory Algorithm may reside in part or inwhole on a memory 338 of the defibrillator. The instruction to theprocessor can be to shock, to not shock, to administer other forms oftherapy, and so on. If the instruction to the processor is to shock, insome external defibrillator embodiments, the processor is configured toreport that instruction to the user via user interface 370, and toprompt the user to do it. In other embodiments, the processor may beconfigured to execute the instructional advice, by administering theshock. If the instructional advice is to administer CPR, the processormay be configured to enable defibrillator 300 to issue prompts toadminister CPR, etc.

Processor 330 can include additional modules, such as module 336, forother functions. In addition, if other component 325 is provided, it maybe operated in part by processor 330 or by another processor.

Defibrillator 300 optionally further includes the memory 338, which canwork together with processor 330. Memory 338 may be implemented in anynumber of ways. Such ways include, by way of example and not oflimitation, nonvolatile memories (NVM), read-only memories (ROM), randomaccess memories (RAM), any combination of these, etc. Memory 338, ifprovided, may include programs containing instruction events forexecution by processor 330 or other processors that may be included inthe external defibrillator. The programs provide instruction events forexecution by the processor 330, and can also include instruction eventsregarding protocols and decision making analytics, etc. that can be usedby advice module 334. In addition, memory 338 can store prompts for user380, etc. Moreover, memory 338 can store patient data.

Defibrillator 300 may also include a power source 340. To enableportability of defibrillator 300, power source 340 typically includes abattery. Such a battery is typically implemented as a battery pack,which can be rechargeable or not. Sometimes, a combination is used, ofrechargeable and non-rechargeable battery packs. Other embodiments ofpower source 340 can include an AC power override, whereby AC power,instead of power from power source 340 is delivered to an energy storagemodule 350 when AC power is available. In some embodiments, power source340 is controlled by processor 330.

Defibrillator 300 additionally includes the energy storage module 350.Module 350 is where electrical energy is stored in preparation for asudden discharge to administer a shock. The charge to module 350 frompower source 340 to the right amount of energy can be controlled byprocessor 330. In typical implementations, module 350 includes one ormore capacitors 352, and may include other circuitry.

Defibrillator 300 moreover includes a discharge circuit 355. Circuit 355can be controlled to permit the energy stored in module 350 to bedischarged to nodes 314, 318, and thus also to defibrillation electrodes304, 308. Circuit 355 can include one or more switches 357. Those can bemade in a number of ways, such as by an H-bridge, and in other ways wellknown in the art.

Defibrillator 300 further includes the user interface 370 for user 380.User interface 370 can be made in any number of ways. For example,interface 370 may include a screen, to display a parameter of a patientthat is detected and measured, provide visual feedback to the rescuerfor their resuscitation attempts, and so on. Interface 370 may alsoinclude a speaker, to issue voice prompts, etc. Interface 370 mayadditionally include various controls, such as pushbuttons, keyboards,and so on. In addition, discharge circuit 355 can be controlled byprocessor 330, or directly by user 380 via user interface 370, and soon.

Defibrillator 300 can optionally include other components. For example,a communication module 390 may be provided for communicating with otherdevices. Such communication can be performed wirelessly, or via wire, orby infrared communication, and so on. In this way, data can becommunicated from the defibrillator 300 to external devices, such aspatient data, incident information, therapy attempted, CPR performance,and so on.

The defibrillator device just described provides one illustrativemedical device that may be used with this disclosure. There are othermedical devices that may be used with this disclosure including thoserecognized by the U.S. Food and Drug Administration for use indiagnosing, preventing, or treating disease or other conditions.

In the treatment of a patient, information of a patient, such asconditions of the patient, temperature, pulse rate, visible signs ofinjury, trauma, etc., or other conditions of the body, may be observedor detected by the caregiver. The caregiver may use that information totake some corrective measure such as administering a treatment to thepatient. The feedback response of the patient to the treatment isobserved or detected by the caregiver and the caregiver may respond tothis feedback by taking further corrective measures. This process ofmaking corrective measures may continue until a point is reached wherefurther corrective measures are no longer required.

In more sophisticated treatment system, a medical device, such as adefibrillator described above and which may be instrumented with ECG orother instrumentation may be used to provide a medical treatment. Themedical device may obtain patient parameter data which may include oneor more of the following measurements: a measurement of CO₂ exhaled by apatient; an electrical activity of the heart of a patient; an exchangeof air between the lungs of a patient and the atmosphere; a pressure ofthe blood in a patient; a temperature of a patient; an oxygen saturationin the blood of a patient; a chest compression of a patient; an image ofthe internal structure of a patient; an oxygen saturation in the bloodin the brain of a patient; the acidity or alkalinity of fluids in apatient; or other patient parameter.

The patient parameter of the CO₂ exhaled by a patient may be measuredusing capnography techniques. The patient parameter of the electricalactivity of the heart of a patient may be measured using ECG techniques.The patient parameter of the exchange of air between the lungs of apatient and the atmosphere may be measured using ventilation techniques.The patient parameter of the measurement of the pressure of the blood ina patient may be measured using non-invasive blood pressure measurementtechniques or invasive blood pressure measurement techniques. Thepatient parameter of the temperature of a patient may be measured usingtemperature measurement techniques. The patient parameter of the oxygensaturation in the blood of a patient may be measured using pulseoximeter techniques or tissue oximetry techniques. The patient parameterof the chest compression of a patient may be measured using chestcompression detection and feedback techniques. The patient parameter ofthe image of the internal structure of a patient may be measured usingultrasound measurement techniques. The patient parameter of the oxygensaturation in the blood in the brain of a patient may be measured usingcerebral oximetry techniques. The patient parameter of the acidity oralkalinity of fluids in a patient may be measured using non-invasive pHmeasurement techniques. These and other techniques and modules forgenerating the foregoing and other kind of patient parameter data foruse with this disclosure are well known in the art. Medical devices mayprovide a caregiver with certain patient parameter or other data. Acaregiver may use this data along with information of a patient that thecaregiver may observe or detect, such as conditions of the patient,temperature, pulse rate, visible signs of injury, trauma, etc., or othercondition of the body, in performing a treatment on a patient.

Medical devices may also be provided with hardware and softwareconfigured to provide a protocol of events that has been programmed intothe medical device for the purpose of providing events for coaching thecaregiver on a treatment to be administered to the patient. The protocolof events may define a decision tree of events that a caregiver shouldfollow for administering a particular treatment.

FIG. 4 shows a decision tree 401 for an illustrative prior art protocol402 by a user. The protocol defines a decision tree of events or rules1-18. Rule 1 may be a selection of the protocol by the user. Eachinternal (non-leaf) node or rule 2-6 denotes a test on an attribute.Each branch or expected outcome a-p represents the outcome of a testprovided by the rules 2-6. Each leaf (or terminal) node 7-18 holds aclass label which is the label for the treatment. By way of example,rule 2 provides 4 branches or expected outcomes which might be aprotocol treatment for treating infection a, a protocol treatment fortreating sepsis b, a protocol treatment for treating trauma c, and aprotocol treatment for treating sirs d. Each of branches a-d lead to newrules 3-6, respectively, which in turn lead to branches or expectedoutcomes which might be protocol treatments e-p. Nodes 7-18 representthe end of the protocol which is the ultimate expected outcome and thelabel for the treatment.

In practice, a protocol may be initiated by caregiver selection of somebranch in the decision tree. The decision tree then typically navigatesthrough the branches of the decision tree based upon that selection.More specifically, a processor of the medical device executes theprotocol to create a processing thread of instructions that navigatesthrough the decision tree. Based on the tests performed at the nodes ofthe tree, the processing thread may issue instructions, alerts, etc., tocoach the caregiver through a medical treatment. These and other likedecision tree constructs provide a decision support system to acaregiver.

Decision support systems have been enhanced in several ways. In someinstances, the decision support system may be dedicated to treatment ofa particular medical condition in which case the medical device may beprovided with more specialized treatment instructions. In others, thedecision support system may be configured to interact with a caregiversuch as by waiting on input from a caregiver before issuing the nextinstruction. This allows the decision support system to factor thefeedback or other input of a caregiver into the next instructions,alerts, etc. provided by the decision support system. In other cases,the feedback or other input of a caregiver may be used to modifydecision rules implemented by the decision support system. These systemsautomatically select clinical guidelines applicable to the care of apatient and track the progress of the patient through a stage of theguideline.

Having thus introduced background on the general operation of decisionsupport systems for use with medical devices, we now turn to featuresthat are provided by this disclosure.

A computing architecture, system and method are disclosed for use in amedical device for providing decision support to a caregiver. Thecomputing architecture includes a memory, a processor in communicationwith the memory, and an instance of a primary rules-based serviceconfigured to provide instruction events. The instance provides aprimary processing thread of instruction events for coaching treatmentof a patient. A software manager module includes an artificialintelligence architecture. The artificial intelligence architecture isconfigured to provide an instance of a conditional rules-based servicefor providing instruction events. The instance of the artificialintelligence architecture provides a processing thread of instructionevents for coaching treatment of a patient that is independent of theprimary processing thread and is configured to trigger an action on theoccurrence of a pre-defined set of input conditions.

The following decision support definition terminology taxonomy isprovided in support of this disclosure.

Decision Support Definitions are what a customer creates using aDecision Support Definition Editor, as defined below, to have all kindsof customer programmable (“Customer Programmable”) help objects such aschecklist, messages, reference material, smart algorithms (“SmartAlgorithms”), drug calculators (“Drug Calculators”), etc. displayed onthe user interface of a customer's medical device(s) (“Customer'sMedical Devices”) and Medical Device Software Applications, definedbelow, when a predefined set of inputs (“Entrance Criteria”) isreceived. The Decision Support Definitions are made up of DecisionSupport Definition Sets and Software Managers, as defined below.

The Decision Support Definition Sets are what the Decision SupportDefinition Editor creates and loads into a user's medical device(s)(“User's Medical Devices”) to work in conjunction with Software Managerscontained within those devices to create the functionality of theDecision Support Definitions. These Decision Support Definition Setsinclude the following types which are defined below: Intelligent Agents,Presentation Definitions, Output Definitions, Reference Material, andSmart Algorithms.

Intelligent Agents encapsulate an entrance criteria (“EntranceCriteria”) definition and execute concurrently with the SoftwareManagers that control the presentation to the users.

Presentation Definitions inform the Software Managers how to displaycustomized user information based on pre-defined presentation tablets(“Presentation Templates”).

Output Definitions inform the Software Managers what output messages tocreate when the user selects each input on a user interface (“UserInterface”) defined by the Presentation Definitions.

Reference Material is information (text and graphics) that can bedisplayed on the User Interface of one of the devices in the disclosedsystem. The Decision Support Definition Editor can decide when ReferenceMaterial is displayed on a device in the system.

Smart Algorithms are individual software algorithms that calculate a newoutput based on a pre-defined set of patient data inputs. Examples ofSmart Algorithms include Smart Vital Indices, Drug Dosage Calculators,and Count-down Timers. The Decision Support Definition Editor can decidewhen Smart Algorithms are initiated on a device in the system. Theoutput can be used as entrance criteria for the Intelligent Agents.

The Software Managers that make up the Decision Support Definitions aresoftware processes that independently operate within the Medical Deviceto perform some type of transformation. There could be one or manySoftware Managers operating within the Medical Device.

External System Elements are the systems that interface with theDecision Support Definition Editor to successfully implement theDecision Support Definitions. The External System Elements include thefollowing elements which are defined below: Decision Support DefinitionLibrary, Device Definition Library, Web (World Wide Web), Monitor orMonitor/Defibrillator, and Medical Device Software Applications.

The Decision Support Definition Library is a collection of DecisionSupport Definitions created and used by the customers of the system.Some of these are designated as shared and available to be review andedited by all customers of the System. The Library can be hosted on acentral server (“Central Server”) or distributed across multiple storagemechanisms owned by the various customers.

The Device Definition Library contains the software and configurationdata of the customer devices. The library can exist on a central serveror locally managed on customer device. They capture the data asregistered and updated version of each device owned by customers. Theyverify the configuration information is correct before updating to thelatest set of Decision Support Definitions by uploading the DecisionSupport Definition Software Sets from the Decision Support DefinitionEditor.

The Web (World Wide Web) is a Repository of shared material (“SharedMaterial”) external to the Medical Device, such as for referencematerial (“Reference Material”) that could be edited and downloaded inthe Customer owned devices.

The Monitor or Monitor/Defibrillator is one class of Medical Device thatDecision Support Definitions can be deployed on to provide theCustomer's programmable help objects. The Software Managers running onthe Monitor use the Decision Support Definition Software Sets todetermine how to implement the Decision Support Definitions.

Medical Device Software Applications are Medical device software thatruns on hardware (“HW”) such as Tablet or Laptop computers, SmartPhones, or other Mobile Devices that contain Software Managers that usethe Decision Support Definitions to provide the Customer's programmablehelp objects.

The Decision Support Definition Editor is what the Customer uses tocreate the Decision Support Definitions that are used by the SoftwareManagers on the customer's devices. It is made up of the followingfunctions (these could be implemented in a variety of ways) definedbelow: a Directory Viewer and an Editor.

The Directory Viewer is used to create new or view and manage thepreviously generated Decision Support Definitions that are stored on theDecision Support Definition Library.

The Editor modifies the Decision Support Definitions. It is made up ofthe following functions. These could be implemented in a variety of waysincluding a Version Control (check-in/check-out), an Entrance Criteria(Intelligent Agent), and a display generator (“Display Generator”)(“Display Manager”).

The Version Control (check-in/check-out) assigns the name and versionnumber to the Decision Support Definitions that are being edited andwhat device type and group the Decision Support Definitions areallocated to.

The Entrance Criteria (Intelligent Agent) defines the entrance criterianecessary to trigger the Decision Support Definitions.

The Display Generator (Display Manager) uses the user Interfacetemplates in the Template Library to define the User InterfacePresentation of the Decision Support Definition on the associated devicetype.

The Result Generator (Response Manager) determines the outputs generatedfrom each user interface control (“User Interface Control”) on theDisplay Generator.

The Smart Algorithms determine the transformation of available inputs tooutputs. The customer has the ability to edit what a smart algorithmproduces based on available inputs.

Device Definition Sets Manager provides an interface into the DeviceDefinition Library to identify all devices that have been registered bythe customer and place those devices into groups for application of theDecision Support Definitions. It also allows updates to the DeviceDefinition Data to be uploaded to the Device Definition Library.

Template Library provides the pre-defined user interface templates(“User Interface Templates”) that are already supported by the SoftwareManagers that operate on the various devices that make up the System.

Decision Support Definition Set Generator uses the inputs entered by theeditor to create the Decision Support Software Definition Sets that areloaded into the customer's devices via the Deployment Manager. Includesconsistency checking that performs a verification that all outputs andinputs are consistent across the Decision Support Definitions and DeviceConfiguration Data that are defined for this device in the DeviceDefinition Library.

Presentation Simulator provides a simulation of the Medical Devices userinterface onto the Editors Platform that will appear on the variousdevices within the customers system. Customer may be able to simulateall of the inputs, defined in the Decision Support Definitions andvisualize the response to those inputs created by the Decision SupportDefinitions.

Deployment Manager provides the capability of the customer to manageapprovals/authorization/authentication of the Decision SupportDefinitions and works in conjunction with the Device Definition Libraryto authorize and track the deployment of new Decision Support SoftwareDefinition Sets.

Turning now to FIG. 5, FIG. 5 illustrates a medical device 510 includinga computing architecture of this disclosure comprising a memory 560, aprocessor 580 in communication with the memory, and an instance module530 including an instance of a primary rules-based service configured toprovide instruction events, the instance providing a primary processingthread of instruction events for coaching treatment of a patient. Asoftware manager module 520 includes an artificial intelligencearchitecture. The artificial intelligence architecture is configured toprovide an instance of a conditional rules-based service for providinginstruction events. The instance of the artificial intelligencearchitecture provides a processing thread of instruction events forcoaching treatment of a patient that is independent of the primaryprocessing thread and is configured to trigger an action on theoccurrence of a pre-defined set of input conditions.

The medical device of this disclosure illustratively further includes acommunication module 540, a user interface 550, and a display 570.

Communication module 540 is hardware and software configured to transmitdata to or from the medical device. Illustratively, the communicationmodule is configured to transmit data from the medical device to anexternal utility. The external utility may be a computer, a laptop, aserver, a mobile computing device, or other computing device.Alternatively, the utility module may transmit data from a utility tothe medical device. The communication module may include a Wi-Ficommunication module 545 configured to provide wireless communication.While illustratively configured for Wi-Fi, the communication module mayemploy one or more wireless communication implementations using anystandard or using a non-standardized wireless implementation. Thecommunication module may further include a Network data communicationmodule 547 configured to provide for hardwire communication. Thecommunication module is configured to provide bidirectional wiredtransmission of data from out of and to the medical device through thecommunication module.

Memory 560 can be any form of data storage. It may be at least one ofrandom access memory (RAM) and/or read only memory (ROM). Informationcan be stored permanently until overwritten and/or stored temporarilyfor use while the unit is active.

Display 570 of the medical device may be a visual display capable ofdisplaying data generated in or transmitted to the medical device.Displays for use with this disclosure may include an LCD screen, ane-paper display, or other bi-stable display, a CRT display or any othertype of visual display.

User interface 550 may include a keypad. In an alternative illustrativeembodiment, the user interface may be a touch screen keypad that isrendered on the display and which allows a user to enter data or to readdata that is rendered on the display. In one embodiment the display isdedicated to providing touch screen keypad functionality. User interfacemay include a speaker, a microphone, manual buttons, or one or moreother controls that allows a user to interact with the medical device.

The medical device may also include a power source (not shown). Toenable portability of the medical device, power source typicallyincludes a battery. Such a battery is typically implemented as a batterypack, which can be rechargeable or not. Sometimes, a combination isused, of rechargeable and non-rechargeable battery packs. Otherembodiments of power source can include an AC power source.

The medical device of this disclosure may communicate with externalutilities which together may provide a patient care system (“PatientCare System”). The medical device of this disclosure may alsocommunicate with external utilities which together may provide adecision support system (“Decision Support System). For purposes of thisdisclosure, the term Decision Support System includes both a PatientCare System and a Decision Support System that is other than a PatientCare System. The external utilities that may be linked to the medicaldevice through communication module may include a series of MedicalDevices, such as monitors and defibrillators, and medical device viewers(“Medical Device Viewers”), such as personal computers, table computers,wall monitors, etc. that operate together to create an advance tool setfor diagnosing and treating patients who are experiencing an emergentcondition. The Decision Support System of this disclosure provides helpto the clinical user in diagnosing patient conditions, determiningappropriate treatment paths, and recording what treatment activitieshave occurred. Help is provided through a series of Smart Algorithms tohelp identify potential patient conditions, Messages/Reminders to informthe user to perform some task or action, a series of checklists helpingthe user follow a pre-defined treatment path, or the display ofReference Material or Calculators to help the user determine propercourse of action or dosing.

The Decision Support System of this disclosure utilizes sets ofconditional rules that are programmed to trigger specific actions by thesystem when certain shared communications are received. In oneillustrative embodiment described in this disclosure the artificialarchitecture uses an Intelligent Agent architecture, in which eachconditional rule is embodied as an Intelligent Agent. However, thedisclosure and claims cover other embodiments that use a differentimplementation architecture, such as a rule engine, expert system, orother artificial intelligence architecture.

Intelligent Agents operate in conjunction with broader Software Managers(e.g., the software process) that control the operation of the MedicalDevices that make up the system. In one illustrative embodiment, dozens,or hundreds of intelligent agents operate with one or more SoftwareManager. These Software Managers that work in conjunction withIntelligent Agents or other embodiments of conditional rules, includebut are not limited to: an algorithm manager that monitors vitals knownas Smart Vitals/Algorithm Manager, an algorithm manager that monitors achecklist known as a Checklist Manager, an algorithm manager thatmonitors an event known as an Event Viewer Manager, an algorithm managerthat provides help known as a Help Manager, an algorithm manager thatmonitors an alarm known as an Alarm Manager, an algorithm manager thatmonitors readiness a medical device or other network resources or assetsknown as a Readiness/Asset Manager, an algorithm manager that monitorsand generates reports known as a Report Manager, and an algorithmmanager that monitors a patient case known as a Patient Case ReviewManager.

These agents may run independently of the main processing threads withinthe Software Manager and trigger action by the Software Manager when apre-defined set of input conditions are true. These Intelligent Agentsmay be customizable by an administrator such as a medical director(“Medical Director”), allowing the customer to decide what agents theywant running, and what response they want from the Decision SupportSystem when the Intelligent Agent input condition is fully met. Thisallows an administrator, like a Medical Director, to select the numberof Decision Support Definitions they want operating in their system andto tailor the specific user interface response when the IntelligentAgent is triggered.

FIGS. 6A-6F show illustrative embodiments of a medical device includinga computing architecture of this disclosure. FIG. 6A shows a medicaldevice 610 including a Software Manager Module 612 which includes anarchitecture involving Intelligent Agents shown in FIG. 6A asIntelligent Agent Module 614. Illustratively, the SW Manager Module alsoincludes the Software Manager as that term has been previously defined.The Intelligent Agent executes concurrently with the Software Managersthat control the presentation to the users.

FIG. 6B shows a medical device 620 including a Software Manager Module622 including an architecture involving an Expert System shown in FIG.6B as Expert System Module 624. FIG. 6C shows a medical device 630including a Software Manager Module 632 including an architectureinvolving a Rule Engine shown in FIG. 6C as Rule Engine Module 614.

FIG. 6D shows a medical device 640 in which a Software Manager Module642 includes a plurality of Intelligent Agents including IntelligentAgent 642 and Intelligent Agent 644. In FIG. 6E, two medical devices652, 656, each including a Software Manager Module shown in FIG. 6E as653, 654, respectively, and each including an Intelligent Agent shown asIntelligent Agent IAM1 654, and Intelligent Agent IAM2 657 communicatebetween themselves over a communication link 655. In FIG. 6D, eachmedical device illustratively serves as an external utility to theother.

FIG. 6F shows a network 670 of medical devices 671, 673, 675, 677, 679,681, 683, and 685 distributed across the network. Each medical device isprovided with a Software Manager Module including an Intelligent Agentand each medical device may illustratively be in communication with oneor more other medical device of the distributed network. As explainedlater, a network may be provided with other external utility devices tomake the system of this disclosure a more powerful decision supportsystem.

Hence, this disclosure provides a computational collaborative decisionsupport system in which a primary decision support system is empoweredwith intelligent agents or other artificial intelligence constructs tohelp a decision-making caregiver make effective treatment decisions or adecision-making team collaborate with each other better to makeeffective treatment decisions.

The collaborative support system of this disclosure is more complex thanconventional individual decision support systems because the processinvolves interactions between the decision-making artificialintelligence architecture, such as agents, and the caregiver and/or teamof caregivers that may require complex software. The tradeoff is a morepowerful decision support system because the decision-making artificialintelligence architecture, such as agents, of the collaborative decisionsupport system of this disclosure may leverage information processingcapabilities of others in providing a caregiver or team of caregiverswith coaching. For example, the collaborative decision support systemmay leverage processing capabilities of information gathering by themedical device or other resources in a network. The collaborativedecision support system may leverage filtering for a particularcondition, such as sepsis, one of the detailed examples explained belowof using the medical device of this disclosure. The collaborativedecision support system may integrate data and processes for use by thecaregiver and/or team of caregivers in treatment of a patient. Thecollaborative decision support system of this disclosure enables betterquality decisions to be made such as in terms of timeliness orcorrectness. The collaborative decision support system of thisdisclosure provides for collaborative situation awareness based upontypes of decision-making tasks.

The collaborative decision support system of this disclosure makesdecisions on what to do next. Each decision-making task of thecollaborative decision support system has certain knowledgerequirements. For example, in a scenario involving treatment of apatient, a caregiver needs to monitor the patient, and to decide how torespond to the conditions of the patient. For this purpose, thecollaborative decision support system provides an instance of a primaryrules-based service to coach the caregiver or team of caregivers. Morespecifically, the instance of a primary rules-based service provides aprimary processing thread of instruction events for coaching treatmentof a patient.

Illustratively, the decision-making provided by the primary thread ofinstruction events provides coaching based upon measured patientparameters such as temperature, heart rate, etc. that is translated intocues internal to the instance of a primary rules-based service regardinghow threatening is the condition of the patient and what is therecommended protocol to follow for treatment of that condition. Therules of the instance of a primary rules-based service specify how tocombine cue values to determine the level of threat and the recommendedprotocol to follow. The instance of a primary rules-based servicegenerates the recommended protocol which may include one or more actionsfor a caregiver to perform. The instance of a primary rules-basedservice may be interactive with the caregiver. For example, the instanceof a primary rules-based service may prompt the caregiver for feedbackfollowing performance of one or more actions which may then be factoredinto the decision tree being processed by the instance of a primaryrules-based service in determining the next action.

Decision-making in treating a patient is typically connected withtreatment hierarchies. A higher-level decision-making task is based upona condition or set of conditions that may indicate a higher threat tothe patient. A lower-level decision making task is based upon acondition or set of conditions that may indicate a lower threat to thepatient. The collaborative decision support system of this disclosuremay organize threats hierarchically according to decision types.

As discussed in connection with FIG. 4, conventional decision supportsystems typically commence by determining the threat of a patient andthen executing a treatment protocol for treating that threat. FIG. 7shows a decision tree 701 for an illustrative protocol 702 of thisdisclosure. The protocol defines a decision tree of events or rules1-18. Rule 1 may be a selection of the protocol by the user. Eachinternal (non-leaf) node or rule 2-6 denotes a test on an attribute.Each branch or expected outcome a-p represents the outcome of a testprovided by the rules 2-6. Each leaf (or terminal) node 7-18 holds aclass label which is the label for the treatment. By way of example,rule 2 provides 4 branches or expected outcomes which might be aprotocol treatment for treating infection a, a protocol treatment fortreating sepsis b, a protocol treatment for treating trauma c, and aprotocol treatment for treating sirs d. Each of branches a-d lead to newrules 3-6, respectively, which in turn lead to branches or expectedoutcomes which might be protocol treatments e-p. Nodes 7-18 representthe end of the protocol which is the ultimate expected outcome and thelabel for the treatment. The foregoing decision tree may be referred toas the “branching decision tree” and would be illustratively executed bythe instance of a primary rules-based service of this disclosure. Theartificial intelligence architecture, such as Intelligent Agents, ofthis disclosure allow for the path navigated through the decision to bechanged based on treatment hierarchies. Hence, the artificialintelligence architecture, such as Intelligent Agents, of thisdisclosure allows navigation through the decision tree to advantageouslychange from one branch to another branch as illustrated in FIG. 7 bynavigational changes A-K.

The artificial intelligence architecture of this disclosure isconfigured to provide an instance of a conditional rules-based servicefor providing instruction events. The instance of a conditionalrules-based service provides a processing thread of instruction eventsfor coaching treatment of a patient that is independent of the primaryprocessing thread and is configured to trigger an action on theoccurrence of a pre-defined set of input conditions. The instance of aconditional rules-based service illustratively continually monitors oneor more conditions in the background and triggers an action that mayoverride the action of the primary processing thread when it involveshigher-level decision-making task. As explained in detail later, theonset of sepsis may be a higher-level decision-making task than a taskinvolving treatment of a wound, for example. The primary processingthread may be instructing the caregiver on treating the wound while theprocessing thread provided by the artificial intelligence architecturemay be monitoring for sepsis. On the onset of sepsis, the processingthread of sepsis treatment steps of the artificial intelligencearchitecture may override the instructions being provided by the primaryprocessing thread of wound caring actions.

In conventional decision support systems, a decision task may beselected by a pre-specified step in a plan. Alternatively, the decisiontask could be requested from a caregiver or a team of caregivers.Likewise, in the artificial intelligence architecture of thisdisclosure, a decision task may be selected by a pre-specified step inby a request from a caregiver or a team of caregivers. Advantageously,in the artificial intelligence architecture of this disclosure, thedecision task may also be dynamically identified by an agent itself. Inthis way, decision-making and information are tightly coupled atmultiple levels. In other words information from the artificialintelligence architecture of this disclosure is integrated into thedecision tree and decision results.

The artificial intelligence architecture of this disclosure may be usedto coach a caregiver performing a treatment, such as in the field or ina hospital setting. The artificial intelligence architecture of thisdisclosure may also be used to support teams where knowledge andexpertise are distributed across a network. For instance, members of ahospital team may have different information, knowledge, and/orexpertise access than a caregiver performing a treatment such as due totheir expertise, roles and responsibility in the team. The team membersmay make decisions at different levels relative to their information,knowledge, and/or expertise that the collaborative decision supportsystem of this disclosure may incorporate into the coaching provided bythe disclosed decision support system. For example, the artificialintelligence architecture of this disclosure information may respond toinformation provided by one or more members of the team with ahigher-level decision-making task and so coach the caregiveradministering the treatment. Advantageously, an agent of this disclosuremay be involved in individual or multiple decision tasks. In either casethe agent may have relations. Hence, the agents of this disclosure maybe configured to effectively manage multiple attentions, that is to say,processes. The design of attention management by an agent of thisdisclosure is a matter of agent design architecture.

We now turn to how to the artificial intelligence architecture of thisdisclosure may represent the threat. We illustrate one representationfor the determination of sepsis. The onset or existence of sepsis is anincreasingly common cause of morbidity and mortality, particularly inelderly, immunocompromised, and critically ill patients. FIG. 8 showssome contributors of sepsis which may include infection, bacterium,fungeria, and systemic inflammatory response syndrome (SIRS) which mayarise from noninfectious pathological causes including pancreatitisischemia, multiple trauma and tissue injury, hemmorrhagic shock,immune-mediated organ injury, and the exogenous administration of suchputative mediators of the inflammatory process as tumor necrosis factorand other cytokines. Early goal-directed therapy is beneficial topatients with sepsis. The artificial intelligence architecture of thisdisclosure may be used to represent this sepsis threat.

According to this disclosure, each threat may be characterized as anumber of parts. FIGS. 9 and 10 show an illustrative way in which athreat of sepsis may be characterized for use with this disclosure. FIG.9 shows an illustrative model 901 for use by an Intelligent Agent in thedetection of sepsis. According to this model, the existence of sepsis isdetected on the presence of “2 or more” of the following rules withdefinitive evidence of infection:

-   -   Temperature>38 degrees C. or <36 degrees C.    -   Heart Rate>90 beats/min    -   Respiratory rate>20 respirations/minute    -   White Blood cell count>12×10{circumflex over ( )}9/L or        <4×10{circumflex over ( )}9/L or with >10% immature forms

As shown in FIG. 9, the sepsis threat illustratively has three parts inthis example: cues, goal, and course of action. The cue are the rules onindividual patient parameters. The rules are shown in FIG. 9 as rules920, 922, 924, and 926 previously explained. The goal is thedetermination that the condition of sepsis exists. The threat analysismakes that determination in the illustrative example based on theexistence of true conditions in two or more of the indicated rules. Thecourse of action is a plan or protocol for treatment of the sepsis oncethe existence of sepsis has been determined. As explained in thisdisclosure, on the existence of the condition of sepsis, by theartificial intelligence architecture of this disclosure, illustrativelyan Intelligent Agent, an alert and instructions may be imposed upon themedical device to make the caregiver aware of the sepsis condition andprovide coaching to the caregiver for treating the sepsis by way of atreatment protocol.

FIG. 10 shows one illustrative correlation 1001 in which the fourparameters shown in FIG. 9 may be correlated to each other to expressthe output rule 910 to have one of the two permissible outputspreviously explained. The rule is expressed again in a statement of therule 1010 which states that:

-   -   Sepsis=f(2 or more rules A, B, C, D)        A logical expression 1020 of this output rule 910 may        illustratively be provided using Boolean logic to express the        output rule 910 in the following form:        If ((Evidence of Infection) && ((A&&B) 11 (A&&C) 11 (A&&D) 11        (B&&C) 11 (B&&D) 11 (C&&D)))        For an Intelligent Agent programed with this rule, logical        expression 1020 provides an output of logical 1 if true or 0 if        not true. If the output is true, the Intelligent Agent has        detected sepsis. In the illustrative example, the Intelligent        Agent alerts the caregiver by an alert event. For example, the        Intelligent Agent may issue a device alert in the form of a        display that “Patient meets conditions for Sepsis” as indicated        by the following instruction in the protocol.    -   {System.out.display (“Patient meets conditions for Sepsis”);}

In this example, the artificial intelligence architecture of thesoftware manager module is providing an instance of a conditionalrules-based service for providing instruction events. In this case, theinstance provided a processing thread of instruction events for coachingtreatment of a patient for patient. This instance was operatingindependently of the primary processing thread and it triggered anaction, namely, the alert, on the occurrence of a pre-defined set ofinput conditions indicative of sepsis.

As indicated, the cue may be the rules on individual patient parametersthat are indicative of the medical condition such as sepsis. The cue mayalso be some elementary data. More particularly, the term “cue” may bean agent's internal representation of the decision situation. The cuesmay be higher-level abstractions of some elementary data. The cues mayalso be some synthesis of lower-level information. In the illustrativeexample of FIG. 9, the cue is a higher-level abstraction of someelementary data. The abstraction takes the form of rules rather than theelementary data in this example. In another illustrative example, thecue may be the root of two or more tree-like information-dependencestructures. The root may describe the way in which the cue is abstractedfrom low-level information. The example in FIG. 9 illustrates the ruleas the root of four tree-like information dependency structures.

FIG. 11 illustrates another way in which the threat may be represented.As shown in FIG. 11, the threat illustratively has four parts: cues,goals, course of actions, and expectancies. The cue are the rules onindividual patient parameters. The goal is the determination of sepsis.The threat analysis makes determinations in the illustrative examplebased on the existence of true conditions in one or more of the rules.The course of action is a plan for treatment on sepsis once theexistence of sepsis has been determined. The expectancies are theexpected outcomes.

More specifically, a prior art protocol for the management of sepsiscomprises a set of rules 1120 that define caregiver treatments 1110 andexpected treatment outcomes 1130. The treatments 1110 may include fluidreplacement, control of blood pressure, tissue perfusion, earlytreatment with antibiotics, administration of steroids for adrenalinsufficiency, control of blood glucose levels, protective lungventilation, and administration of activated protein C.

The set of rules 1120 comprise individual rules 1121-1128. Rule 1122,for example, is a rule for the treatment control of blood pressure 1140and provides for a test on an attribute referred to as the mean arterialblood pressure (MAP), an attribute well known in the art. As shown inFIG. 11, if the outcome of the MAP test is equal or greater than 65 mmHg after fluid replacement, then the branch or expected outcome 630 ofthat test might be to continue monitoring MAP. If, however, the outcomeof the MAP test is less than 65 mm Hg after fluid replacement, then thebranch or outcome of that test might be to adjust dosage ofnorepinephrine. The leaf (or terminal) node in this case might hold aclass label which is the treatment and is denoted as the control ofblood. For an Intelligent Agent programed with these rules, logicalexpression 1120 provides an output of logical 1 if true or O if nottrue. If the output is true, the Intelligent Agent has detected theindicated condition. In the illustrative example, the Intelligent Agentalerts the caregiver by an alert event. For example, if rule 1121 weretrue, the Intelligent Agent may issue a device alert in the form of adisplay that “Patient meets conditions for Fluid Replacement” asindicated by the following instruction in the protocol generated by theIntelligent Agent.

-   -   {System.out.display(“Patient meets conditions for Fluid        Replacement”);}

As previously described, the collaborative decision support system ofthis disclosure places no restriction on the sources of decision tasks.It could be a pre-specified step in a plan, could be a request from ahuman partner or other teammates, or could be dynamically identified byan agent itself. Hence, the decision task may be selected by apre-specified step in a plan. The decision task may be requested from acaregiver or a team of caregivers. Advantageously, in the artificialintelligence architecture of this disclosure, the decision task may bedynamically identified by an agent itself.

To initialize the process, from the current situation, the collaborativedecision support system determines the collection data that arepotentially relevant to the task. In one illustrative embodiment, thecaregiver or any one of a team of caregivers select the decision taskthat provides the protocol for treatment. The collaborative decisionsupport system explores the decision space hierarchy to pick one that isapplicable to the decision task. A decision process is chosen and fixedas the primary processing thread of instruction events for coachingtreatment of a patient based on a primary rules-based service. Aninstance of a primary rules-based service provides a primary processingthread of instruction events for coaching treatment of a patient. In thebackground, the artificial intelligence architecture provides aninstance of a conditional rules-based service for providing instructionevents. The instance provides a processing thread of instruction eventsfor coaching treatment of a patient that is independent of the primaryprocessing thread and is configured to trigger an action on theoccurrence of a pre-defined set of input conditions.

If the pre-set defined set of input conditions does not occur, thecollaborative decision support system continues processing the primarythread of instruction events for coaching treat of a patient based on aprimary rules-based service. However, if the pre-set defined set ofinput conditions occurs, the instance of a conditional rules-basedservice of the artificial intelligence architecture of the collaborativedecision support system provides instruction events. The instanceprovides a processing thread of instruction events for coachingtreatment of a patient that is independent of the primary processingthread and is configured to trigger an action on the occurrence of apre-defined set of input conditions.

An intelligent agent may trigger an action on the medical device, suchas an alert, or rendering of an instruction of a treatment protocol on adisplay of the medical device. For instance, referring again to FIG. 11,the task of tissue perfusion is a treatment that involves severalsub-tasks. One is the assessment for evidence of adequate tissueperfusion such as heart rate, respirations, urine output, mentation.This sub-task may be assigned to a first intelligent agent. Anothersub-task is the monitoring of Scv02 which may be assigned to a secondintelligent agent. Another sub-task is the monitoring of hematocritwhich may be assigned to a third intelligent agent. In this example,three intelligent agents are monitoring for different conditions. Thefirst intelligent agent may output its assessment as data rendered on adisplay of the medical device. The second intelligent agent may alsooutput its assessment as data rendered on a display of the medicaldevice. The third intelligent agent may create an alert on theoccurrence of the condition that it is monitoring (i.e., Scv02 is lessthan 70% after CVP and MAP corrected. The fourth intelligent agent mayoutput its assessment as data rendered on a display of the medicaldevice.

The intelligent agent may also trigger an action in another agent. Forinstance, the task of control of blood pressure involves monitoring meanarterial pressure, known as MAP, involves the sub-tasks of monitoringMAP and adjusting dosage of norepinephrine if MAP<65 mm Hg after fluidreplacement. The task of control of blood pressure may be assigned to afirst agent. Alternatively, the sub-task of monitoring MAP may beassigned to one agent and the task of control of blood pressure may beassigned to a second agent. In this example, the output from the firstagent may be applied as an input to the second agent. When the MAP dropsbelow 65 mm Hg, this input from the first agent causes the second agentto generate an alert to the caregiver to adjust the dosage ofnorepinephrine.

To initialize the collaborative decision support system in anotherillustrative embodiment, the caregiver, any one of a team of caregiversor the artificial intelligence architecture of this disclosure mayselect the decision task that provides the protocol for treatment. Afterselection, the collaborative decision support system proceeds aspreviously described. This allows for the artificial intelligencearchitecture of this disclosure to override a selection of the caregiveror any one of a team of caregivers.

In the foregoing and other examples, the primary processing thread andthe processing threads of each artificial intelligence architecture,such as intelligent agents, are executing effectively contemporaneously;albeit illustratively one or a subset of the processing threads may beeffectively triggering an action on the medical device at one point intime. These threads may be organized within a decision space in ahierarchical fashion such that the higher-priority threads have priorityover threads of lower priority. For example, a processing thread of anartificial intelligence architecture, such as intelligent agents,associated with sepsis, may have a higher priority than a primaryprocessing thread currently being executed in which event the higherpriority processing thread associated with sepsis will execute toprovide actions to the caregiver or team of caregivers instead of theactions being provided by the primary processing thread once theprocessing thread of the artificial intelligence architecture hasdetermined the existence of sepsis. If there are two processing threadsassociated with one or more artificial architectures (i.e., which maycome from the same or different agents, expert systems, rule enginemodules, or other artificial architectures) that have a higher prioritythan the primary processing thread currently being executed, then asbetween the two threads, the one with the higher priority may executefirst to provide actions to the caregiver. After the higher prioritythread has executed the actions called for by that instance, thecollaborative decision support system may determine which processingthread now has the higher priority.

In one illustrative embodiment, a caregiver or a team of caregivers mayover-ride any thread that is being executed or manually select anotherthread to execute from the decision space hierarchy in which the may beorganized. The agent of this disclosure may derive informationrequirements pertaining to a current decision task from the cues underits consideration. A plurality of agents in a team illustratively playdifferent roles when investigating a situation. For example, one agentmay play the role of monitoring for sepsis while another agent may playanother role. A plurality of agents distributed across a network mayplay the same or different roles. For instance, the agent for monitoringsepsis may be distributed across the network in a plurality of medicaldevices in a network. An agent for monitoring assets may be centrallylocated in the network. Hence, a plurality of agents distributed acrossthe network are seen to play the same role than one or more agentsperforming different roles across the network.

In addition, the agents while trying to synthesize the availableinformation into appropriate cues, may ask for help from some potentialinformation providers. For instance, an agent may ask a caregiver orteam of caregivers for certain information. On receiving thatinformation from teammates, the agents may synthesize the new acquiredinformation to develop better situation awareness. The agents may alsolearn new information-dependency trees from a caregiver or team ofcaregivers, and revise experiences in its experience knowledge databaseto incorporate additional cues. The agents may also revise experiencesin its experience knowledge database based on new information from otheragents.

Network resources, including experts, who have the experiences relevantto a current task may tell the agents about the information relevant tothe cues that need to be considered. Since agents may have differentexperience knowledge databases and information-dependence structures,the agents and network resource can inform other agents and networkresources about the cues that have been synthesized in ways beyond theexisting expertise of the agent and the network resource. In addition,the agent's network resources may share experiences by informing thecues and other resources of important information. An agent that has noexperience pertaining to a current task may help another agent uponbeing requested of certain information. For example, an agent may replyto another agent with information that is synthesized in a way differentfrom the structure being considered by the other agent.

An important part of an investigation process is evolving recognitions.The anytime instances of this disclosure allow an agent to trigger anoutput at any point of the investigation process, so long as the agenthas attained a satisfactory level of awareness of the current situation.

The agents of this disclosure may be configured to trigger an output ofa current situation based upon a past experiences that most matches thecurrent situation. For example, an agent may include a set of pastexperiences for sepsis such as sepsis conditions according to age, sex,race, etc. The agent may execute a process thread based upon a match ofthe current situation with one of these past experiences. If thesituation faced by the agent is an unfamiliar situation, the task of theagent may be more than cue-driven information investigation. The agentmay need to identify a collection of cues which the agent may synthesizeusing prescribed rules.

For adaptive learning according to this disclosure, an administrator mayconsider both the positive and negative evidences collected in one ormore artificial intelligence architecture, such as intelligent agents,and to reconcile conflicting evidences reported by different artificialintelligence architectures, as well as the caregiver and/or caregiverteam members, if any. Certain human-adjustable thresholds can be used tojudge whether a rule, hypothesis for the situation, or the like areapplicable to the current situation. If not, the administrator mayreconfigure the artificial intelligence architecture to generateadditional evidences which may be used to explore another rule,hypothesis for the situation, or the like until the administrator findsan acceptable one. Once the administrator proves the rule, hypothesisfor the situation, or the like works, the rule, hypothesis for thesituation, or the like will be stored as a valid experience for dealingwith future similar situations.

As part of this disclosure, the administrator may synthesize experiencesof two or more past experiences to deal with an unusual situation.Synthesis may be based on experiences from one or more artificialintelligence architecture, such as intelligent agents, and thecomponents of a synthesized experience may typically involve thecoupling of the corresponding parts of the input experiences.

After an artificial intelligence architecture, such as intelligentagents makes a recognition, it will illustratively continuously monitorthe associated expectancies until the completion of the selected courseof actions. This expectancy monitoring is important to adaptivedecision-making. An artificial intelligence architecture, such asintelligent agents, can subscribe information relevant to theexpectancies that need to be continuously monitored. The informationsubscribed may include patient parameter data. It may also include datafrom the caregiver, caregiver team, and/or other resources to take fulladvantage of the team's distributed cognition. In this way, theartificial intelligence architecture, such as intelligent agents, maytrigger the appropriate task when the situation arises and terminate theactions that may have resulted from a wrong recognition at the earliestopportunity.

The artificial intelligence architecture, such as intelligent agents,may use expectancies to initiate complete new decisions. For example, ifthe expectancy is that sepsis exists on the occurrence of a predefinedset of conditions, such as explained above, the expectancy serves as agate-condition for the artificial intelligence architecture, such asintelligent agents, to keep following the current recognition. Inaddition, the artificial intelligence architecture, such as intelligentagents, may use expectancies to refine a decision. Illustratively, theartificial intelligence architecture, such as intelligent agents, mayleverage some structures within the active experience knowledgedatabase. If the artificial intelligence architecture monitoring anexpectancy, such as sepsis, has detected the expectancy, but in fact thecondition of sepsis does not exist in the patient contrary to theexpectation, this invalidation of the expectancy may indicate that theonce workable recognition is no longer applicable to the changingsituation.

If the artificial intelligence architecture, such as intelligent agents,has already executed part of the selected course of actions, thoseactions may still make sense since the error in the expectancy was notknown at the commencement of the course of action. However, theartificial intelligence architecture, such as intelligent agents, maymake adjustments to the expectancy. The adjustments may be made on thefly by the caregiver and/or team of caregivers or other resources. Forinstance, if the expectancy is dependent on the temperature of apatient, the temperature may be a variable that a trained medical expertmay remotely or at the site adjust in a particular situation; therebyadapting the expectance of the artificial intelligence architecture,such as intelligent agents, on the fly. An administrator may also makeadjustments to the expectancy, also on the fly or after the fact. Ineither case, the adapted expectancy of the artificial intelligencearchitecture, such as intelligent agents, can be used for furtherrecognition. If the adaption occurred in real time, the adaptedexpectancy enables the artificial intelligence architecture, such asintelligent agents, to start another round of recognition, using therefined experience to develop a better solution.

FIG. 12 shows an illustrative embodiment 1201 of a device alert 1220that the medical device may provide to a caregiver on detection by theDecision Support system of this disclosure of a condition known assystemic inflammatory response syndrome, otherwise known as SIRS. Thedevice alert 1220 is one or more pieces of data about the condition ofthe patient rendered on a display 1205 of a device (not shown). Thedevice is illustratively a smart phone; but may be any other mobiledevice including a laptop, a notebook, a PDA, or any other mobilecomputing device. The device may also be a non-mobile device like apersonal computer, a monitor, or any other computing device. The display1205 may be any display provided by the device for rendering the devicealert. It will be appreciated that the device may communicate the devicealert by other than visual rendering. For example, device may include aloudspeaker for communicating an alert to the caregiver as an audible.Any user interface configured for communicating an alert to thecaregiver is contemplated by this disclosure.

The device alert 1220 illustratively includes alert information 1230alerting the caregiver of the existence of a disturbance. The alert datamay include data that conditions 1231 have been met, particular data1232 on events that have triggered the condition, and or one or moreinstruction data 1234 for the caregiver to follow in administering atreatment to correct the condition. FIG. 12 shows two instructions. Thefirst instruction is step 1 which is “assess fluid status, monitor CVP,administer 500-mil fluid every 20-30 minutes.” The second instruction isan “acknowledge completion of step 1.” The user may acknowledge thecompletion of step 1 by manual activation of a button on the medicaldevice. If the display 1205 is a touch screen, the “acknowledge” may bean active button which the caregiver may activate to signal the medicaldevice that step 1 is complete. On completion of step 1, the cascadecontrol system of this disclosure may display another instruction asstep 2 and receive an acknowledgment from the caregiver on completion ofthat step of the treatment; and so on.

In this example, as previously explained, the Intelligent Agent isfeeding these instructions for treatment of SIRS for rendering on thedisplay in response to the Intelligent Agent having detected the onsetor existence of SIRS. The instructions that the Intelligent Agent may bestreaming to the caregiver may illustratively include those detailed inFIG. 11 as those conditions may be been detected by the IntelligentAgent which have been previously explained and displayed on device alert1220 shown in FIG. 12. In this regard, instruction 1234 displayed inFIG. 12 will be recognized as rules 920, 922, and 924 in FIG. 9. As anext instruction for display on device alert 1220, the device alert 1220may display the instruction associated with rule 1122 in FIG. 11 for thecontrolling of blood pressure. By providing one or more instruction ondevice alert 1220, the device alert 1220 may navigate the caregiverthrough the instructions that are generated from rules 1121 through 1128shown in FIG. 11 for the treatment of sepsis. As previously explained,while these instructions are being fed and displayed to a caregiver in aparticular order for action, the artificial intelligence architecture ofthis disclosure is continually monitoring the outcomes of each otherrule of the protocol so that should any violation of any of these rulesbe detected while an outcome from another rule is being fed anddisplayed to the caregiver, the processing thread of the agent taskedwith monitoring the violated rule may issue a correction for that error.

The instruction associated with rule 1121 shown in FIG. 11 furtherillustrates the task management function of the Intelligent Agent, whichtask management function may occur as a background or a foregroundapplication. Rule 1121 calls for a challenge to the caregiver every20-30 minutes. The artificial intelligence architecture, likeIntelligent Agents, of this disclosure may manage this task bycalendaring the event on an internal calendar and prompting thecaregiver at the expiration of the 20 minutes. The agents may work withthe Software Managers to control the information that is provided to theuser. In this way, not only does the disclosed decision support systemprovide instructions to a caregiver on steps to take through a branchdecision tree for a treatment, but the disclosed decision support systemmay manage all tasks that may be associated with instructions includingcalendaring, prioritizing, managing those tasks, and issuinginstructions to the caregiver when it comes time for that task to beperformed by the caregiver.

FIG. 13 shows an alternative embodiment 1301 of a Decision Supportsystem of this disclosure. The embodiment 1301 includes a device 1310, adisplay 1315, and a device alert 1315 which includes an alert 1321 forthe purpose of prompting the caregiver about the existence of acondition, data 1321 on the condition itself; in this case the onset orexistence of SIRS in a patient, data 1324 on the identity of thepatient, and an instruction 1326 instructing the caregiver to evaluatefor sepsis. In this illustrative embodiment, the disclosed controlsystem merely prompts the caregiver to evaluate for sepsis; leaving itto the caregiver to determine and administer the steps for the treatmentof sepsis. In this case, the instance of a primary rules-based servicemay be put into a pause mode, suspending further navigation through thebranch of the branch decision tree for the treatment prior to the alertof the condition. When the caregiver acknowledges that he is finishedwith the sepsis evaluation, the instance of a primary rules-basedservice may come out of pause mode, causing the instance of a primaryrules-based service to continue navigating through the branch of thebranch decision tree to continue the treatment that was ongoing at thetime of the disturbance.

FIGS. 14A and 14B show further illustrative embodiments of a medicalsystem including a computing architecture of this disclosure. FIG. 14Amore particularly shows a simplified diagram of a patient care system(“Patient Care System”) according to this disclosure. System 1401includes the devices that are in the patient vicinity 1410 used by theprimary care givers and remote viewers (“Remote Viewers”) that are usedby remote caregivers illustrated as remote caregiver 1420 and remotelocation 1430 in FIG. 14A. Devices in the patient vicinity 1410 mayinclude a monitor, a monitor/defibrillator, along with Medical DeviceSoftware Applications that can operate on a variety of a tablet or PCComputers, and other mobile devices. In a personal area network, theyshare information directly using various forms of wired or wirelessconnectivity. To support the Wide Area connection, they may sharecommunications through a cloud-based Internet Communication System 1402.These Software Applications contain various Software Managers (softwareindependent processes) in software manager modules 1412, 1414, 1417 thatcontrol the information that is presented to the user on each specificdevice, and determine what controls and information each user has accessto. Information is shared bi-directionally through the system to allowinformation entered on any Medical Device or Medical Device SoftwareApplication to be shared with any other Medical Device in the system.For example, monitor 1411 may communicate with tablet 1413 overcommunication link 1420 and with local display 1416 over communicationlink 1419. These devices may each communicate over communication link1402 through cloud 1402 to reach remote location 1430 and/or remote caregiver 1420 or other external utilities. Each Medical Device maintainsits own active view of the shared distributed communication system.

FIG. 14B shows a simplified block diagram of a Decision Support systemaccording to this disclosure. Decision Support System 1440 comprises aseries of Software Managers shown in FIG. 14B as Smart Vital/AlgorithmManager 1452, Help Manager 1454, Checklist Manager 1456, Event ViewerManager 1458, Asset Manager 1460, Alarm Manager 1462, Report GenerationManager 1464, and Patient Care Review Manager 1466. The function ofthese managers has been previously described. Each Software Manageroperates independently as part of the user interaction (“UserInteraction”) solution for the system. Each Manager is responsible for acertain type of information that is provided to one or more users viathe User Interface of the specific Medical Device. The actualimplementation can include one or more of these Software 38 Managers.Each Software Manager is designed to allow manual input from the user,or the Software Manager may have a series of associated IntelligentAgents which are scanning a shared memory 1480 of the device todetermine if a pre-defined inputs or user-configured conditional hasoccurred and should trigger an action. These pre-defined inputs couldinclude Vital Sign values, Event communications, device state parameters(including identifying sensors disconnected from a patient or notcorrectly reporting vital sign data), Patient Demographic data, or anyof the other categories of shared information communicated across thesystem. The system may include a cloud based communication system 1470.Communication links 1491-1498 may be established by the associatedSoftware Managers to allow the Software Managers to reach other SoftwareManagers distributed across the network. These distributed SoftwareManagers may reside in remote servers, computers, mobile communicatingdevices, or the one or more medical devices being used by a caregiver toadminister a treatment. The response to a trigger might includedisplaying a message to the operator, setting a system variable to apre-defined value, or sending a shared message across the DecisionSupport system. Any of these outputs could cause another IntelligentAgent within a different Software Manager to trigger an additionalresponse. This secondary response could occur with an Intelligent Agentin the same or different Medical Device within the system.

Software Managers include, but are not limited to a: Checklist Manager,an Event Viewer Manager, a Smart Vitals/Algorithm Manager, Help Manager,an Alarm Manager, a Readiness/Asset Manager, a Report Manager, and aPatient Case Review Manager. These managers have been previouslydescribed. Each of these Managers control a type of information that ispresented to the User via the device User Interface. Each device in thesystem contains a Software Application that can run some or all of theseSoftware Managers depending on the type of information presented to theuser on that Medical Device type. Additional Software Managers can beadded at a later time to provide additional information to the user orSoftware Managers could be combined in any form.

The Intelligent Agents are self-contained, reactive, and associated withthe various Software Managers. They operate independently of the mainthreads of these Software Managers. They are interconnected via a shareddistributed communication system that supports both the Medical Devicesand Remote Viewers. The Intelligent Agents have execution dispatched bythe operating system, by operating system middleware, or by an executionengine. Intelligent Agents independently process the shared informationreceived and if the right values, combination and sequence of inputs arereceived to match the user pre-defined conditional, encapsulated withinthe agent, they trigger a pre-programmed response by their associatedSoftware Manager. Example responses to triggers include generatingalarms, sending messages, logging events, activating or deactivatingsensors or actuators, initiating user input or output, or any otheraction deemed appropriate by the software manager's implementation. Theresponse to a trigger could also be a change in the contents of sharedmemory, which may in turn lead other Intelligent Agents from the same ordifferent Software Managers to trigger and initiate additional actions.The reacting agents may be within Software Managers that are locatedlocally on the Medical Devices, or on one of the Remote Viewers of thesystem, allowing a coordinated response to patient care across a localarea (such as within an ambulance or hospital room) or broadgeographical area (such as within the area served by an EMSorganization, hospital group, or collection of care givers within aregion).

Both system agents and user-configurable agents are allowed. Systemagents are fixed within the system as part of the design, and usuallyused to ensure safe operation of the device or to alert the necessarypersonnel in case of problems. The user-configurable Intelligent Agentsare more focused on the patient, and are configurable by the MedicalDirector (Customer) to create the sequence of inputs and trigger actionsthat they want to occur in the specific Software Managers across thevarious Medical Devices.

When configuring an Intelligent Agent, customers can decide what ManualInputs and shared data are available to stimulate the Agent and whatspecific response the Agent will trigger within the associated SoftwareManager. The data may include both instantaneous events or datareadings, as well as state information (such as mode of the system) orhistorical data (e.g. data trending). The specific response and what isdisplayed to the user may also be tailored by the Medical Director. Inthis manner, the Medical Director has full control over what aspects ofthe Decision Support System they want to implement, and what specificoutputs are provided by the Software Managers when Intelligent Agentstrigger within the Medical Device. The Medical Director can implement aslittle or as much of this system as they see fit.

To accomplish this customer tailoring, the Decision Support Systemincludes a Decision Support Definition Editor that can be used by theMedical Director to create this customization. The Decision SupportDefinition Editor allows the Medical Director to create the list ofIntelligent Agents, and their Manual Inputs, action trigger and resultedUser Interface presentation. The Editor allows the Medical Director tospecify which instances of the Software Manager this specificIntelligent Agent is to work with.

The Intelligent Agents are illustratively architected to be Metadata-driven, so that no code ever needs to be compiled when a customercreates a configuration. Thus the Decision Support Definition Editorcreates a Decision Support Definition Set that can be downloaded to eachallocated device. This Support Tool Software Set could be implementedusing a markup language such as XML, or implemented in some other typeof Meta Data structure. The Intelligent Agents configure themselvesbased on the Medical Device configuration, and execute according to theneeds of the configuration. Through this process the user can decidewhat specific inputs and data are available, how the device responds tothose inputs and data, and exactly what action is produced, includingcontrol what is displayed on the screen, what messages get sent, whatdata or events get logged, what lights get controlled, and what sensorsand actuators get activated or deactivated.

FIGS. 15A, 15B, and 15C show an illustrative example of the decisionsupport system of this disclosure. As shown in FIG. 15A, the processbegins at start 1502. At step 1504, the process begins taking patientvalues The patient values may be patient parameter values such as CO₂exhaled by a patient; electrical activity of the heart of a patient;exchange of air between the lungs of a patient and the atmosphere;pressure of the blood in a patient; temperature of a patient; oxygensaturation in the blood of a patient; chest compression of a patient;image of the internal structure of a patient; oxygen saturation in theblood in the brain of a patient; acidity or alkalinity of fluids in apatient; or other patient parameter. The methods for measurement ofthese patient parameters have been previously described. The patientvalues may also be values that are input by a user such as risk factorsdenoted in step 1508. At step 1506, the process determines if inputparameters are missing. If they are not missing the process advances tostep 1534 shown in FIG. 15B. If they are missing the process advances tostep 1508 to determine if risk factor(s) information is applicable. Riskfactors are values selected or input by the user that denote the levelof risk of a certain event, such as the risk of heart disease. These mayinclude sex, age, family history of heart disease, post-menopausal,race, smoking, cholesterol level, obesity, etc. If they are notapplicable the process advances to step 1516 described below. If theyare applicable, at step 1510, the user selects the risk factors upondevice prompt or selects none. If applicable risk factor(s) are selectedthe process advances to step 1516 described below. If the user selectsnone, at step 1512, the process determines whether it can triggerwithout a risk factor input. If it can trigger without a risk factorinput, at step 1514, the process disables the secondary controller orprocessing thread of the artificial intelligence architecture for thispatient encounter. If it can trigger without a risk factor input theprocess advances to step 1516. At step 1516, the process determineswhether vital sign information is applicable. If vital sign informationis not applicable, the process advances to step 1534 shown in FIG. 15B.If vital sign information is applicable, the process advances to step1518. At step 1518, the user connect needed vitals upon device prompt orselects “vital not available.” The connections the user would be madeare those required to determine the patient parameters or vitals thatare needed for the particular treatment being processed. If vitals arenot available, the process advances to step 1530 shown in FIG. 15B.Otherwise, the process advances to step 1534 shown in FIG. 15B.

Referring to FIG. 15B, at step 1530, the process determines whether thesecondary controller or processing thread of the artificial intelligencearchitecture cannot trigger without a vital sign input. If the secondarycontroller or processing thread of the artificial intelligencearchitecture can trigger without a vital sign, the process advances tostep 1532, where the secondary controller or processing thread of theartificial intelligence architecture is disabled for this patientencounter. If the secondary controller or processing thread of theartificial intelligence architecture cannot trigger without a vitalsign, the process advances to step 1534, where the process determineswhether the secondary controller or processing thread of the artificialintelligence architecture is to be triggered on a dedicated ornon-dedicated unit. A dedicated unit may be the user interface 370described in FIG. 3 or it may be a user interface on another computingdevice which is configured with a secondary controller or processingthread of the artificial intelligence architecture client, also referredto herein as a Native Notification Client, for receiving secondarycontroller alerts. If the secondary controller or processing thread ofthe artificial intelligence architecture is to be triggered to adedicated unit, the process advances to step 1540. If a non-dedicatedmonitor is to be used, the process advances to step 1536 where theprocess is configured with the settings for the Native NotificationClient of the non-dedicated unit and at step 1538 the user navigates tothe secondary controller or processing thread of the artificialintelligence architecture for more information. At step 1540, theprocess determines whether it will be receiving recommendations from thesecondary controller or processing thread of the artificial intelligencearchitecture with or without a link to a protocol. If it will receiverecommendations without a link to a protocol, the process advances tostep 1560 in FIG. 15C. If it will receive recommendations with a link toa protocol, at step 1542, the user selects the link to a protocol orselects close. If the user selects close, the process advances to step1560 in FIG. 15C. If the user selects link to a protocol, the processadvances to step 1544 where the complete protocol is linked to theprocess after which the process advances to step 1560 in FIG. 15C.

Referring to FIG. 15C, at step 1560, the process determines whether theartificial intelligence is to be adapted, also referred to herein asAccuracy Assessment. If the Accuracy Assessment is turned off, theprocess advances to step 1568 described below. If the AccuracyAssessment is turned on, the process advances to 1564 where a userselects whether to keep the Accuracy Assessment on, turn it off, ordelay the enablement of the Accuracy Assessment. If the delay isselected, the process advances to step 1562 where a user selects aperiod of time to delay the enablement of the Accuracy Assessment. Thedelay may be 5 minutes, 30 minutes, 3 hours, some other period of time,or upon a device prompt such as by a user. The delay introduces a delayin the enablement of the Accuracy Assessment for the selected period oftime. On time-out, the process returns to step 1564 to determine whetheranother delay is needed or whether the process may continue with theAccuracy Assessment being enabled or disabled. If at step 1564, the userselects the Accuracy Assessment to be disabled the user the processadvances to step 1566 where the user selects or free type enters thecorrect recommendation to make or selects unknown. The recommendationsentered would be the corrections that a user might make to the decisionsupport system 1401 shown in FIGS. 14A, 14B based upon operation of theprocess. An unknown recommendation might be a situation where an errorwas observed by the user but the user may think of no recommendation tomake to correct the error. An unknown recommendation may also beimportant since as previously explained, these outputs are provided toan administrator of the decision support system 1401 shown in FIGS. 14A,14B by wired or wireless techniques, who may have the expertise to makea recommendation to correct the error based upon experience,information, or data. For example, the administrator may compare theerror to historic data on the process flow such as data collected frommedical devices using the processes which may be stored in a database.As another example, the administrator may research authorities forinformation on how to correct the error that may unknown to the user.

At step 1568, an administrator such as a medical director may log onto awebsite's process creator to view Accuracy Assessment Data. At step1570, the administrator may make changes to the process. At step 1572,the administrator may deploy those changes to the medical device 1420 inFIG. 14 and/or one or more similar devices that are being administeredby the administrator. This completes the adapting of the artificialintelligence as previously explained and ends the process at step 1574.

FIGS. 16A, 16B, and 16C show a process for creating processes for use inthe decision support system of this disclosure, deploying the processesto one or more medical devices, and making and deploying changes to anydeployed process whether triggered by the adaptive intelligence of theartificial intelligence architecture of this disclosure in use or by anupdate of a process by the administrator. Referring first to FIG. 16A,the process begins at step 1602 and advances to a step 1604 where adecision is made whether to use administer defined Ad-hoc rules,administer defined institutional rules, or use commercially developedrules in the process to be deployed. If an administrator defined Ad-hocrule is selected the process advances to step 1606 where theadministrator selects one or more devices to create or edit a secondarycontroller on. At step 1608, the administrator turns on the device andnavigates to a simple secondary creator editor. At step 1610, theadministrator decides to create or edit the secondary controller. Ifcreate is selected, the process advances to step 1630. If edit isselected, the process advances to step 1630.

If, at step 1604, the administrator selects institutional rules for theprocess, at step 1612, the administrator illustratively logs onto awebsite of institutional rules. Alternatively, the administrator maycollect that information from other sources. At step 1614, theadministrator navigates to the second controller or processing thread ofthe artificial intelligence architecture creator page which takes theprocess to step 1610 previously described.

If at step 1604, the administrator selects commercially developed rulesfor the process, at step 1616, the administrator logs on a website ofcommercially developed rules or collects that information from othersources. At step 1618, the administrator navigates to the secondcontroller or processing thread of the artificial intelligencearchitecture creator page. At step 1620, the administrator decides toacquire or modify the setup options. If modify setup options isselected, the process advances to step 1670. If acquire is selected, theprocess advances to step 1683.

Referring now to FIG. 16B, at step 1630 the administrator decideswhether to create or edit the secondary controller or processing threadof the artificial intelligence architecture. If create is selected, theadministrator proceeds to create one or more rules that the secondarycontroller the artificial intelligence architecture executes such as thefollowing rule for sepsis previously described in connection with FIG.10.

-   -   If ((Evidence of Infection) && ((A&&B) 11 (A&&C) 11 (A&&D) 11        (B&&C) 11 (B&&D) 11 (C&&D)))

The process involves the steps of adding a name 1034, selecting a signto use for the first logic statement 1636, selecting a logic symbol toadd the first logic statement 1638, selecting the vital sign value toadd to the first logic statement 1640, selecting the logic symbol to addbetween first and second logic statements 1642, and repeating steps 1634through 1642 for each additional logic statement that makes up the rule.

For the above indicated rule for sepsis, and with reference to FIG. 9,starting with rule A&&B above which translates to A AND B, which wouldbe combining rule 920 and rule 922 in a Boolean AND operation, the stepof adding a name 1034 would be “temperature and respiratory rate.” Thestep of selecting a sign to use for the first logic statement 1636 wouldbe “>” and “<” in rule 920 and “>” in rule 922. Referring still to FIG.9, the step of selecting a logic symbol to add to the first rule 920would be the Boolean OR operation since temperature must be >38 degreesC. or <36 degrees C. For rule 922 there would be no sign to use sincethe rule requires only that the respiratory rate by > than 20respirations/minute And to both rule 920 and rule 922 together, theadministrator would add the Boolean And operation since this is theBoolean operation required between rule 920 and rule 922 represented asA and B in the following fragment (A&&B) of the above-identified rulefor sepsis. The step of selecting the vital sign value to add to thefirst logic statement 1640 would be “38 or 36” for rule 920 and “20” forrule 922. The step of selecting the logic symbol to add between firstand second logic statements 1642 would be the Boolean AND operationrepresented by && which performs the Boolean AND operation of rule 920and rule 922 in FIG. 9. The administrator would then repeat steps 1634through 1642 for each additional logic statement that makes up the rule.In other words, the administrator would do the same for (A&&D), (B&&C),(B&&D), and (C&&D) in the above rule for determining the onset orexistence of sepsis.

At step 1646, the administrator decides if a patient risk factorqualifier is needed. If no, the process advances to step 1648 describedbelow. If a patient risk factor qualifier is needed, the administratorselects or creates the patient risk factor qualifiers to add to thesecondary controller and advances to step 1650. At step 1648, theadministrator selects the secondary controller or processing thread ofthe artificial intelligence architecture recommendation and advances tostep 1648 where the user selects Ad-hoc rules and advances to step 1660or selects institutional rules and advances to step 1674 both describedbelow.

If at step 1630 the administrator selects edit to edit a secondarycontroller or processing thread of the artificial intelligencearchitecture rule that is already part of the process, the creatoradvances through steps 1652, 1654, and 1656 where the administratorselects the secondary controller or processing thread of the artificialintelligence architecture rule to edit, selects the attributes of therule to modify, and modifies the attributes, respectively. The processthen advances to step 1658 for the administrator to select whether touse Ad-hoc or institutional rules as previously described.

Referring finally to FIG. 16C, when coming the institutional rulesbranch of step 1658 of the branch for using administrator defined ad hocrules or user-defined with institutional rules branch (see step 1604 onhow to enter those branches), at step 1674 the administrator selects theAccuracy Assessment, which is the adapting of the artificialintelligence, on/off. At step 1676, the administrator selects thedistribution destinations, at step 1678, the administrator selectswhether to have any defaults on or off, at step 1680, the administratorselects implementation of the secondary controller, which transmits thechanges to the selected controller(s) or processing thread of theartificial intelligence architecture wirelessly or by wire, after whichthe process ends at step 1694.

When coming from the modify branch of step 1620 of the branch for usingcommercially developed rules (see step 1604 on how to enter thatbranch), at step 1670, the administrator selects the default on/offoption, and at step 1672, which distribution destinations to send thesecondary controller or processing thread of the artificial intelligencearchitecture to, which transmits the changes to the selectedcontroller(s) or processing thread of the artificial intelligencearchitecture wirelessly or by wire, after which the process ends at step1694.

When coming from the acquire branch of step 1620 of the branch for usingcommercially developed rules (see step 1604 on how to enter thatbranch), at step 1682, the administrator determines whether apre-packaged secondary controller or processing thread of the artificialintelligence architecture is desired. If none is available, theadministrator logs off the website at step 1692. If a prepackagedsecondary controller or processing thread of the artificial intelligencearchitecture is available, at step 1684, the administrator identifiesthe system software version needed for the secondary controller orprocessing thread of the artificial intelligence architecture desired.At steps 1686, 1688, and 1690, the administrator identifies the devicesto distribute the secondary controller or processing thread of theartificial intelligence architecture to, connects those devices to thenetwork, and performs a software update, respectively. The process endsat step 1694.

From the foregoing it is seen that a computing architecture, system andmethod are disclosed for use in a medical device for providing decisionsupport to a caregiver. The computing architecture includes a memory, aprocessor in communication with the memory, and an instance of a primaryrules-based service configured to provide instruction events, theinstance providing a primary processing thread of instruction events forcoaching treatment of a patient. A software manager module includes anartificial intelligence architecture. The artificial intelligencearchitecture is configured to provide an instance of a conditionalrules-based service for providing instruction events. The instanceprovides a processing thread of instruction events for coachingtreatment of a patient that is independent of the primary processingthread and is configured to trigger an action on the occurrence of apre-defined set of input conditions.

The artificial architecture may be an intelligent agent configured witha conditional rule. The artificial architecture may be a rule engineconfigured with a conditional rule. The artificial architecture may bean expert system configured with a conditional rule.

The instance of a conditional rules-based service may be configurable byan administrator of the computing architecture. The configurability ofthe medical device may enable the administrator to configure theconditional rules-based service to a particular environment. Theconfigurability of the medical device may enable the administrator toconfigure the conditional rules-based service to provide a particularuser interface response. The configurability of the medical device mayenable the administrator to configure the conditional rules-basedservice to operate on a set of distributed, interconnected medicaldevices. The set of distributed, interconnected medical devices supporta plurality of system configurations. The configurability of the medicaldevice may enable the administrator to configure the conditionalrules-based service to cover a predetermined number of patientconditions. The configurability of the medical device may enable theadministrator to configure the conditional rules-based service to covera predetermined number of treatment paths.

The medical device may further include a decision support definitioneditor configured to provide the instance of a conditional rules-basedservice for providing instruction events.

The medical device may further include a user interface and a softwaremanager responsible for triggering a predetermined action on the userinterface. The user interface may be a display; and the predeterminedaction on the user interface is a rendering information on the display.The artificial intelligence architecture of the software manager moduleof the medical device may be further configured to provide one or moreadditional instances of a conditional rules-based service for providinginstruction events, the one or more additional instances providing oneor more additional processing threads of instruction events for coachingtreatment of a patient that are independent of the primary processingthread and are configured to trigger an action on the occurrence of apre-defined set of input conditions.

The medical device may further include a user interface. The userinterface may be configured for receiving input data for configuring thecomputing architecture. The intelligent agent may be configured to scanthe memory of the medical device to determine if a predefined input or auser configured condition has occurred and should trigger an action.

The software manager module may provide a first software manager; andmay further include a second software manager module including anartificial intelligence architecture. The artificial intelligencearchitecture may be configured to provide an instance of a conditionalrules-based service for providing instruction events. The instance mayprovide a processing thread of instruction events for coaching treatmentof a patient that is independent of the primary processing thread and isconfigured to trigger an action on the occurrence of a pre-defined setof input conditions. The artificial architecture of the second softwaremanager module may be an intelligent agent configured with a conditionalrule. A predefined input or a user configured condition may trigger anaction by the second software manager module. The first software managermodule may trigger a first action; and the first action may be appliedto the second software manager module to trigger a second action.

The software manager module may include a software manager selected fromthe group consisting of smart vital algorithm manager, a help manager, achecklist manager, an event viewer manager, an asset manager, an alarmmanager, a report generator manger, and a patient care preview manager.The instance of a conditional rules-based service for providinginstruction events may be for detection of sepsis. The instance of aconditional rules-based service for providing instruction events mayinclude a set of rules including:

-   -   if ((Evidence of Infection) && ((A&&B) 11 (A&&C) 11 (A&&D) 11        (B&&C) 11 (B&&D) 11 (C&&D)))    -   wherein:    -   A=Temperature>38 degrees C. or <36 degrees C.    -   B=Respiratory rate>20 respirations/minute    -   C=Heart Rate>90 beats/min    -   D=White Blood cell count>12×10{circumflex over ( )}9/L or        <4×10{circumflex over ( )}9/L or with >10% immature forms &&=the        Boolean logical operator AND    -   11=the Boolean logical operator OR

The instance of a conditional rules-based service for providinginstruction events may include a device alert. The device alert may berendered on a display of a device selected from the group consisting ofa smart phone; a lap top, a notebook, a PDA, a personal computer, amonitor, and any computing device.

A medical system of this disclosure may include a medical device, aninstance of a primary rules-based service, a software manager module,and an external utility. The medical device may include a computingarchitecture. The computing architecture may include a memory, aprocessor in communication with the memory, and a communication module.The instance of a primary rules-based service may be configured toprovide instruction events. The instance may provide a primaryprocessing thread of instruction events for coaching treatment of apatient. The software manager module may include an artificialintelligence architecture. The artificial intelligence architecture maybe configured to provide an instance of a conditional rules-basedservice for providing instruction events. The instance may provide aprocessing thread of instruction events for coaching treatment of apatient that is independent of the primary processing thread and isconfigured to trigger an action on the occurrence of a pre-defined setof input conditions.

The external utility may be in communication with the medical device.The external utility may be configured for exchanging data between themedical device and the external utility.

The medical system may further include a shared data base. A softwaremanager may be provided. The software manager may be selected from thegroup consisting of a smart vital algorithm manager, a help manager, achecklist manager, an event viewer manager, an asset manager, an alarmmanager, a report generator manger, and a patient care preview manager.The software manager may be configured to access the shared database.

The external device may be a computer. The computer may be a server. Thecommunication between the medical device and the external device may bevia a network. The network may be the Internet.

The external device may be a computer selected from the group consistingof a server, a personal computer, a tablet, a mobile computing device, avideo device, an ultrasound device, and a printer. The external devicemay be in communication with the medical device through an access point.The communication from the external device is by an intelligent agent;the intelligent agent providing interpretation of data from the medicaldevice. The communication from the medical device may be to an assetmanagement registry intelligent agent, the asset management registryagent configured to manage assets of the system. The communication fromthe external device may be by decision support server.

A method for providing decision support for a medical treatment mayinclude the steps of providing a primary processing thread ofinstruction events for coaching treatment of a patient based on aprimary rules-based service; providing a processing thread ofinstruction events for coaching treatment of a patient that isindependent of the primary processing thread for coaching treatment of apatient based on a conditional rules-based service; triggering an actionby the independent processing thread of instruction events based on aconditional rules-based service on the occurrence of a pre-defined setof input conditions.

The method may further include the step of configuring the instance of aconditional rules-based service by an administrator. The configurabilityof the medical device may enable the administrator to configure theconditional rules-based service to a particular environment. Theconfigurability of the medical device may enable the administrator toconfigure the conditional rules-based service to provide a particularuser interface response. The configurability of the medical device mayenable the administrator to configure the conditional rules-basedservice to operate on a set of distributed, interconnected medicaldevices.

The set of distributed, interconnected medical devices of the method maysupport a plurality of system configurations. The configurability of themedical device may enable the administrator to configure the conditionalrules-based service to cover a predetermined number of patientconditions. The configurability of the medical device may enable theadministrator to configure the conditional rules-based service to covera predetermined number of treatment paths.

The method may further include the step of configuring the conditionalrules-based service for providing instruction events. The method mayfurther include the step of triggering a predetermined action on a userinterface based upon the action triggered by the independent processingthread of instruction events. The user interface used in the method maybe a display. The predetermined action on the user interface may be arendering information on the display.

The method may further include the step of triggering a predeterminedaction on a user interface by a software manager. The software managermay be selected from the group consisting of smart vital algorithmmanager, a help manager, a checklist manager, an event viewer manager,an asset manager, an alarm manager, a report generator manger, and apatient care preview manager. The instance of a conditional rules-basedservice for providing instruction events may be for detection of sepsis.The instance of a conditional rules-based service for providinginstruction events may include a device alert. The device alert may berendered on a display of a device selected from the group consisting ofa smart phone; a laptop, a notebook, a PDA, a personal computer, amonitor, and any computing device.

The method may further include the step of transmitting the triggeredaction to an external utility. The external device may be a computer.The computer may be a server. The communication between the medicaldevice and the external utility may be via a network. The network may bethe Internet. The external device may be a computer selected from thegroup consisting of a server, a personal computer, a tablet, a mobilecomputing device, a video device, an ultrasound device, and a printer.

The artificial intelligence architecture of this disclosure providesdevice driven recommendations of a patient's health and care path thatare based on mathematical assessments of multiple input parameters suchas physiological vital signs and health related risk factors. They mayimprove caregiver response time, alarm accuracy, and to aid in givingcaregivers ideas of potential next steps. The computing architecture ofthis disclosure provides a Decision Support System using IntelligentAgents that are distributed across multiple devices through sharedCommunications. The Decision Support System is Modular, made up of aseries of Intelligent Agents (or other conditional rules engine) thatmay run within Software Managers. The Decision Support System isinterconnected through a series of bidirectional distributed sharedCommunications.

The Decision Support System is available to run on many differentMedical Devices across distributed system. The Decision Support Systemmay support a wide variety of Medical Device configurations. TheDecision Support System may operate on integrated devices working in aLocal Area or on remote devices that are supported through our CloudBased Internet System. The Decision Support System may include systemrules programmed into each individual device. The Decision SupportSystem may be tailored by the Customer (End User) to determine whichparts run on which device.

The Decision Support System may be tailored by the Customer to determinewhich elements to include, what manual inputs are provided, whattriggers are included and exactly what appears on the screen(checklists, messages, reference material, etc.). The Decision SupportSystem may be used to support a broad range of patients who areexperiencing a vast array of Medical Emergencies and the supportingtreatment paths for each of these emergencies.

This disclosure extends the current state-of-the-art for Medical DeviceDecision Support systems. Medical Device Decision Support systems thatcurrently exist typically operate on a single set of MedicalEmergencies, with treatment paths editors that are specific to apredefined configuration of Medical Devices. This disclosure is uniquein form, by creating a simplified Decision Support System that ismodular, interconnected, and can fully be tailored to the needs of manyCustomers.

This use of a series of integrated Intelligent Agents allows a DecisionSupport System that may be customized to work on a broad set ofdistributed inter-connected Medical Devices supporting numerous systemconfigurations. This Decision Support System is applicable to a broadset of Medical Emergencies, covering a number of patient conditions andsupporting treatment paths.

The Decision Support System of this disclosure may use multiplephysiological patient parameter input data to trigger a device response.The Decision Support System may analyze user (clinician, medicaldirector, etc., entered patient risk factors, patient demographicinformation, and/or past patient event data to trigger the deviceresponse. The user may create the rules, including the physiologicalpatient parameters, the parameters' trigger values, and the applicablepatient risk factors, patient demographic information, and/or pastpatient event data to be considered. The decision controller may triggera device response that provides a caregiver with notification of therule triggered. The Decision Support System response may include apatient diagnosis. The Decision Support System response may include arecommended course of action for the clinical user to consider taking inrespect to the patient. The Decision Support System response may becommunicated remotely from the patient to a personal computer, a tablet,a smart phone, a server, or other computing device.

In the adaptive intelligence part of this disclosure a user is requestedto enter an accuracy assessment of the Decision Support System responsecompared to the actual patient condition. The user entered “accuracyassessment(s)” may be used to make a recommendation to the caregiver ofa parameter or device response that could be changed to improve theaccuracy of the decision tree controller in the future. The user entered“accuracy assessment(s)” may be used to automatically update thedecision tree controller. Multiple “accuracy assessments” across manypatients and separate institutions may be used to make changes to thealgorithms parameters or device response to improve its accuracy. Thetriggered instruction events and resulting information may be stored asan event in the patient log. The triggered instruction event may causeadditional decision support features to be propagated automatically tothe user including but not limited to protocol assistant, reminders,etc.

The Decision Support System allows users to enter their own rules thatmay be specific to their usage needs and as the science matures. Thedecision tree controller may include additional input parameters overjust the common real time monitored physiological parameters. They willleverage user entered patient risk factors. The Decision Support Systemmay give recommendations of a possible course of action for the user totake based on the information received. (e.g., Evaluate Patient forSepsis). The Decision Support System may be made available to remoteviewing at devices such as laptops, tablets, smart phones. The DecisionSupport System may give the user the ability to assess the accuracy ofthe instruction event output compared to the actual patient conditionpost the triggered event. These accuracy assessments may allow foradaptive rule improvements and may allow for more sophisticated rulesthat can look at extended patient history, trends across patients andthe population, etc.

In this description, numerous details have been set forth in order toprovide a thorough understanding. In other instances, well-knownfeatures have not been described in detail in order to not obscureunnecessarily the description.

A person skilled in the art will be able to practice the presentdisclosure in view of this description, which is to be taken as a whole.The specific embodiments as disclosed and illustrated herein are not tobe considered in a limiting sense. Indeed, it should be readily apparentto those skilled in the art that what is described herein may bemodified in numerous ways. Such ways can include equivalents to what isdescribed herein. In addition, the disclosure may be practiced incombination with other systems. The following claims define certaincombinations and subcombinations of elements, features, steps, and/orfunctions, which are regarded as novel and non-obvious. Additionalclaims for other combinations and subcombinations may be presented inthis or a related document.

We claim:
 1. A method for providing decision support for a medicaltreatment, comprising: providing a primary processing thread ofinstruction events for coaching treatment of a patient based on aprimary rules-based service; providing a processing thread ofinstruction events for coaching treatment of a patient that isindependent of the primary processing thread for coaching treatment of apatient based on a conditional rules-based service; and triggering anaction by the independent processing thread of instruction events basedon a conditional rules-based service on the occurrence of a pre-definedset of input conditions.
 2. The method of claim 1, further comprisingconfiguring an instance of a conditional rules-based service by anadministrator.
 3. The method of claim 2, wherein the configurability ofthe instance enables the administrator to configure the conditionalrules-based service to a particular environment.
 4. The method of claim2, wherein the configurability of the instance enables the administratorto configure the conditional rules-based service to provide a particularuser interface response.
 5. The method of claim 2, wherein theconfigurability of the instance enables the administrator to configurethe conditional rules-based service to operate on a set of distributed,interconnected medical devices.
 6. The method of claim 5, wherein theset of distributed, interconnected medical devices supports a pluralityof system configurations.
 7. The method of claim 2, wherein theconfigurability of the instance enables the administrator to configurethe conditional rules-based service to cover a predetermined number ofpatient conditions.
 8. The method of claim 2, wherein theconfigurability of the instance enables the administrator to configurethe conditional rules-based service to cover a predetermined number oftreatment paths.
 9. The method of claim 8, wherein the instance of aconditional rules-based service for providing instruction events is fordetection of sepsis.
 10. The method of claim 8, wherein the instance ofa conditional rules-based service for providing instruction eventsincludes a device alert.
 11. The method of claim 10, wherein the devicealert is rendered on a display of a device selected from the groupconsisting of a smart phone; a lap top, a notebook, a PDA, a personalcomputer, a monitor, and a computing device.
 12. The method of claim 1,further comprising configuring the conditional rules-based service forproviding instruction events.
 13. The method of claim 1, furthercomprising triggering a predetermined action on a user interface basedupon an action triggered by the independent processing thread ofinstruction events.
 14. The method of claim 13, wherein the userinterface is a display, and wherein the predetermined action on the userinterface is a rendering information on the display.
 15. The method ofclaim 13, further comprising: triggering a predetermined action on auser interface by a software manager, wherein the software manager isselected from the group consisting of smart vital algorithm manager, ahelp manager, a checklist manager, an event viewer manager, an assetmanager, an alarm manager, a report generator manager, and a patientcare preview manager.
 16. The method of claim 1, further comprisingtransmitting the triggered action to an external utility.
 17. The methodof claim 16, wherein the external device is a computer.
 18. The methodof claim 17, wherein the computer is a server.
 19. The method of claim16, wherein communication between the medical device and the externalutility is via a network.
 20. The method of claim 1, wherein the networkis the Internet.
 21. The method of claim 1, wherein the external deviceis a computer selected from the group consisting of a server, a personalcomputer, a tablet, a mobile computing device, a video device, anultrasound device, and a printer.